{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np \n",
    "import matplotlib as mpl \n",
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns \n",
    "import matplotlib\n",
    "\n",
    "# matplotlib.use('TkAgg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1040x800 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### 箱形图(Box Plot)\n",
    "# 箱形图是可视化分布的好方法,记住中位数,第25个第45个四分位数和异常值.\n",
    "\n",
    "# import data\n",
    "df = pd.read_csv(r\"F:\\PythonProject\\AI\\Python\\DataAnalysis\\datasets\\mpg_ggplot2.csv\")\n",
    "\n",
    "# Draw Plot\n",
    "plt.figure(figsize=(13,10),dpi=80)\n",
    "sns.boxplot(x='class',y='hwy',data=df,notch=False)\n",
    "\n",
    "# Add N Obs inside boxplot (optional)\n",
    "def add_n_obs(df,group_col,y):\n",
    "    medians_dict = {grp[0]:grp[1][y].median() for grp in df.groupby(group_col)}\n",
    "    xticklabels = [x.get_text() for x in plt.gca().get_xticklabels()]\n",
    "    n_obs = df.groupby(group_col)[y].size().values\n",
    "    for (x,xticklabel), n_ob in zip(enumerate(xticklabels),n_obs):\n",
    "        plt.text(x,medians_dict[xticklabel]*1.01, \"#obs :\" + str(n_ob),\n",
    "                horizontalalignment='center',fontdict={'size':14},color='white')\n",
    "        \n",
    "add_n_obs(df,group_col='class',y='hwy')\n",
    "\n",
    "# Decoration\n",
    "plt.title('Box Plot of Highway Mileage by Vehicle Class', fontsize=22)\n",
    "plt.ylim(10,40)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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159xrail87yta2Wer5S8qScn3W8LvtXxzUsn/fvRNUibemOD5TefcqlZLpmasmn6/Xm9lO28MyrTn2Ig04TKzA83srmAgjS/MrCHm+/T9oFj89+lp+WR5tKRnzA+UslMbb/V48Hyq+cFhTrYkA6EAbSHpwjbPeZ855/4qf9+PDZJGyddKxQrvel/VyrrWqal2IeGVLefcS/I1XKHbnXOtJTepir058kfyQ+g+Jz8S1F7OuT+nuJ4d1HRCkHRb5ZurKCjbYiTDrZTKvnbyNYTStncVMdy+Tc65z1opV93KvNDnrcyrD56bJcXOuQ/l+/NITbVX4XN4olsRNz1MLt9yMUM3S1JwgWKBpPvkv0Phicg6NR2TYW1eolFB7w7mH25me8RMPy94fsE512K0xXa6N3g+x5rfm+u+DK0/3mA1naD2V+u1pqGe4R9buW/D46y14yjZvB3DENqIOUzoe8SvIEWt/cbEzov/jkd5zDwq33TR5PsYSZLMrK987Z+UoJbLzHqoKRHqp9b3W3j+1FOJJfte18f8nerFrvDYykTCJaV/bCTbxkTCi4+Z/p/SyMymyF9UOk++/2BP+c87/D6FLQyafZ+cc2/LJ2Sb5H8P75dUaX5UybvNbIziOOcWyLfKqJd0knwT7rVm9o6Z3WJm+8QvAyRD0oUOxTlXJd9XSPKdYhMWS3V1iSYGzQ5Pjpk0JkM1RbE3R97RObebc26cc+5nzrmkN3ttw1Ztawbk+v2jkg833q4InsNkanzc9Iq46fHzY/1Uvmnil/IdzIc457o75wa6ppt1hyf3LbY9uPr+VPBystR4s+KzgmlbXcsVY658P7zD5Wu1B8rXqCZqopkJXWL+3qetmtPgURGzzFbt2wzEvTbFmKM4ppOuM8pjJmhi1niLgZimZN+W7/NTp6bmoLFiP+ujU9xv2+I9vsLt/DDFbSxMY91hM9ieZrZnhuOWmQ2XbxJp8s0fD5Af3GKHmO/TbWHx+OWdc3dL2kX+ouwj8v0Gh8jXuL5gZjclWOYaSXtI+ol8H8gNwetLJS0xs8vilwESIelCRxQmKPH3n1kbPJcmWzBoNhD2B1ubpNj/yjcfWi3/43ugmvon5YP18h3JpVa2NWbeFvm+V5mUyr42NTX/SLav81V4X58ewdXzZHZsZd7Wqgiex8f059osP9iD5IfxrpVvYmVqPekK+/Vd65y7NegX08jMusg3XWzNncHz2WZWKH9T70HyTUhb9KFsr6CfzB/kT6imBpPvzdT6E1gT83d7rmpvzb4NvxetHUfJ5oVxDwz68EQlvglXsnkt7oWlaI+ZsInhrmrq2xP28fpL0KqhmaD5eFhLkk81GGHNdHubgMYLj43BQd+/TFqopoto32qtYDudKn/u+oZ8k/tXnHN1cWWKWy7WxDm3xjl3m3PuZOfcIPmWMX8MZl9mZi36sDnnVjrnfu2c+y/5WrzD5JsrFkj6jZnttXWbhc6ApAsdUXiiH/9DHN5geEIryx4RPDv5e/I0Ewxs8W35ZjGTJP0gmFVuZoe3K9oMC05K3wheHtFK0XDekrh/Wo2DQqTR2TheuK8PtQQ3kw6Mlh/hTEqt71M+ie0LcliiAmbWU2nezDpNFcHz7vIjHQ6QH16+Vmq82v+PYPrRaqU/l/yVXinBMR/4mnwNQWv+Il9js6N8M7qwr84fXBs3R22HsClhV/mmQqk2vU1b0D8ubOaWaNCHtmzNvg2XSXiMBcYlmf5CzN+nJCmTCcneX2pK9D+VH60zXmTHjPM33H0zeHlOUOsSNh+bmXgpSU37rT2fdVQWB8/7WPtuLh4v3EZT02AtGeGc+7eaBgv5oSUekKoFM0v1fDT8Pr2eqC9hsJ60+uk6516VdKb8qIdqa3nnXINz7nn5iwQb5Ierb+07Ckgi6UIHE1y1C+8OH38iH1453cfMTo6bFw5A8f+Cl0/G99UJOmSHfbmmOucWOecekL/qXiBpVhu1HtkUbmtZon/SZraLmvo6/DFudmxfhJT+YSbwsHwN2vby97WJf3+Tv6G05EeTXNLO98kJ5wdNCZOXHyc5YbhYifs/ZSqG2H5dVwfP8aP3VQTP1wTPLfpzBTYEz/GjJCqogbguhXjq1XQye4V8oidltmlh+F4vy2/TjZJ+6JxrrV9cJtwTPE80syNbK2hm8X1ZtmbfPhw8nxAMMBG//DAlGX3U+UFH/hK8vNLMWq11NbO0R/EMDDOzM+Inmh9dMuxX++dEJ8hZOGbCdZ+qpsEV1sg3EUsm/KzHBBfZkkrwWUflafn+cQWSbtyKi2GSWjTDv8rMWq0Zasex8VP5WvedJP3RWg5qFb/+A+RbkKQi/D4NTzL/IsUM2hL3PskuAIZ9jDcFLxtHP25tGfmWBGEfvUQjJgPNkHShQzBvlHwb7bDzebOhyp1zi+X7g0jSfcFoYkXB8rtJekx+6Ok6SVfGrb+L/A19+8jX4lwdM/t78k0Nhyr1Id2j9jtJ/5a/j9j82Fq44O/58p2PV0maHrfsO2qqJfyu2iG42nlH8PIGM7s8/McbnPzdK39lW2pKdLc1VwfPh0u6PxwFy8x6mNml8gOgZLrZZryK4Hl03OtU54fCvjVXmtm3guM9HEluXrB8KjUPd8nXlB4s//9lSZAgZZxz7mrn3I+cc3dFsf44d8hfxCmQNNfMfmxm4e+MzGx7MzvBzP6spr5Eoa3Zt3+Q/z52lfS4mX09POE2s7HyJ86bW4n7cvkBBgZLWmxmpwU1sGHcO5vZuWb2oppq7dO1QdJdwe9p15hte1z+N/FLNTUDTSTKYya8xcB28v3/JOn+INlL5kE1JWUzzew68yN/SvKjWZrZUWZ2r6K7RUEzQUuEy4OXEyU9ZmYjY2IaYGanmtlDaaz2MvkLbCWSXjSzU4KBRMJ1DjWz883sH5IuTDPeJZIukL/wdrSk18xscmxyF/xOfsPMHpAfFCO+O0Ay4fdpPzO7ObzQaWZ9zaxc/lYdyUYSnm5m/xd8VxsTZjMbaGZT1TRa6JMxy8wzs9+b2ZHBhYRwmZ3l79vZP9jOp1OMH52Zy4ObhfHgkcpDyW+O/JFibhAq/wP40yTr2EG+z0tYtlb+xDh8XafEN/r8mZpu+LhngvmHq+mGnC2WT3G7KtqxT8K4xyeYt7/8Vd2wTE3wCF9/KGnfJOudGbfcyuBxWRqxdVfTDTjDfbte/gQrnPaTJMte3d59Erf8yhTKjo+JZ1iyeUmWjb0BckOwfXXB6z/IN4NzkqYnWHalktzANdUy8iP3he//peJuxhp8BptjypyeZD3D5PvchOX+I38y7eRPWs9OJd5gXU/ErOeS9nx+MeuqUJKb2LZ3ObXj5sjBvGJJz8eUcfK/HZ/HTXs6k/tWvoZsXczyG+X7PIWjf04O/t6cZF+MVtPNgMP3XKfmv5lOSX4zU9jH18s3V0v2ezoxhXVl7JhJsO7H4rZzeArLbKfmv10u+Mw+U/Pfr+Vxy8XeHLk0ybqTllGSmyPHzL9UTTedjz8WnKR1ceWT3hw5mD8mOIbij41Ncdue8Hc6hf14jJpu6hwb86dquW8vjFs26c2R1XTT7/AR9mN28rW71yfajzHrjH3f+O/vzXHLxH7nG4LYN8ZNuzyTxyyPjvugpgvbovibIxfL//i9I980ZLRz7peJFnTOrZdve32x/InCJvkan9Xyica+zjcZbGRmB0m6Knh5uUtwryzn3HNqugHpHdby3jBZ53w79X3krzIvk2+/b/L9HK6XP/lI1qzve5KuDZbrIn/FeqjSaG7ofJ+ib8mPRrZA/p9bb/kk+UFJY5xzN6S9YXnEOXe9pGPl7/32haQiSUvkmzKdqab91dqw8lujIubvF1zczViDz+DFmEmJ+nPJObdSvv/ZfWrqtL9Rfnjkw5xz6QzHHjaJ2yx/ktMhON9c73D5Pp3z5C9a9JLvz/G+/DH9HcX1kdnafeuce0PSvvKDTlQF77devoZ6lJpqUxMeY87f4uKrkn4kf7+vDfLHZb1838//lXSUpPZ+F2vl+8D8XH4/dA/ie1TSwc65OSmsI8pjZmbM3/9yzi1tawHn3BfOuRMkfVO+v2ClfPOx7vL/K+bKD1ee1X48zrlb5Y+Fu+T3dYH8Sf9y+VqXtG5073zrj69I+h/55CI8Nurkj43fyd8Y+cZk62hj/U/K12CdJ98KJRzyvod8sveEfA3kMOfc79NY9WlBzMvkL2IUyN8X7Qfy/ay2JFnuavkaw3ny5wsm/5lWyn8fj3bOxY9E+AP5pq/z5W/jUiT/HVwhf6yOcc61GPEQSMScc7mOAQA6nKAZ2Cr5jt9nO+dm5TikrDCzP8gnJv/nnPtOruPp6MzsOvk+NAudc60NEpS3OGYAdAbUdAFANL4jn3Btka8J6/DMbLD8DUSlln0FkWHBwEHhzYTn5zKW9uKYAdBZkHQBQDuZ2Q1BZ/OSmAEO+pvZf8s3AZL81fuq3EWZHcEADbfLN8N62Tm3KMchdQhm9l9m9mszG2lm3YJpXc3fS6hCvnn1ejWNurfN4JgB0JnQvBAA2snM/qam+53Vyg9O0E++r4DkR+U6xvl7PXVIZjZF0hT5UUOL5Gv2xjnn/p7TwDoIM5skP3Kq1DR4x3byfVsl3z/sZOfcNlPTxTEDoDOipgsA2u8G+XsKLZUf5XE7+VqHZ+U7YB/ekROuQB/5+/HUyyeZx3HynFHPSvqF/EAHVfKDd/xHfvCEW+UHxNlmEq4AxwyAToeaLgAAAACIUGGuA0hXt27d3MCBA9suCAAAAAARqaqq+o9zrlsqZbe5pGvgwIGqrKzMdRgAAAAAOjEzW5tqWfp0AQAAAECESLoAAAAAIEIkXQAAAAAQIZIuAAAAAIgQSRcAAAAARIikCwAAAAAiRNIFAAAAABEi6QIAAACACJF0AQAAAECESLoAAAAAIEIkXQAAAAAQIZIuAAAAAIgQSRcAAAAARIikCwAAAAAiRNIFAAAAABEi6QIAAACACJF0AQAAAECESLoAAAAAIEIkXQAAAAAQIZIuAAAAAIgQSRcAAAAARIikCwAAAAAilLWky8yuMjNnZsOD1yvN7C0zey14nJ6tWAAAAAAgWwqz8SZmNkrSIZJWx806xTm3NBsxAAAAAEAuRF7TZWbdJN0h6fuSXNTvBwAAAAD5JBvNC38h6X7n3IoE8x4wszfM7G4zG5hoYTObYmaV4aOmpibaaJEVH3/8sT7++ONch4EM4fPsOPgsOw4+y46Dz7Lj4LPsvCJNusxsjKSDJP0uwezDnXMjJY2S9Imk+xKtwzk3zTlXGj569+4dXcAAAAAAkGFR13SNk/RVSSvMbKWkUklPmdmxzrnVkuScq5N0s6TDIo4FAAAAALIu0qTLOTfVOVfinBvmnBsmqVLS0ZKeM7N+MUXPkPRqlLEAAAAAQC5kZfTCBIolzTGzLpJM0geSynIUCwAAAABEJqtJV1DbFdo/m+8NAAAAALmQtZsjAwAAAEBnRNIFAAAAABEi6QIAAACACJF0AQAAAECESLoAAAAAIEIkXQAAAAAQIZKuQGVlpS666CJVVlbmOhQAAAAgLZzL5jeSrsCMGTO0bNkyzZgxI9ehAAAAAGnhXDa/kXTJXxlYsGCBJGnBggV5eYWgtrZWl1xyiU488UQtX7481+EAAAAgT+TLuWxdXZ0uv/xyfetb39Ibb7yRkxjyFUmX/JWBggK/KwoKCvLyCsHdd9+t22+/XY899pguuuiiXIcDAACAPJEv57KzZ8/WzTffrLlz5+q8887LSQz5iqRLUkVFherr6yVJ9fX1qqioyG1ACfTp0yfh3wAAAOjc8uVcdrvttmv8m/PV5gpzHUA+GD9+fOPBWlhYqPHjx+c6pBYmTZqkmpoarV69WlOmTMl1OAAAAMgT+XIue+qpp+rOO+/U+++/r8suuywnMeQrki5JkydPbmwH29DQoMmTJ+c4opbMTN/73vdyHQYAAADyTD6dy55//vk5e+98RvNCSaWlpZowYYIkacKECSotLc1xRAAAAEBqOJfNf9R0BSZPnqzq6uq8rOUCAAAAWsO5bH4j6QqUlpZq+vTpuQ4DAAAASBvnsvmN5oUAAAAAECGSLgAAAACIEEkXAAAAAESIpAsAAADSNtLjAAAgAElEQVQAIkTSBQAAAAARIukCAAAAgAiRdAEAAABAhEi6AAAAACBCJF0AAAAAECGSLgAAAACIEEkXAAAAAESIpAsAAAAAIkTSBQAAAAARIukCAAAAgAiRdAEAAABAhEi6AAAAACBCJF0AAAAAECGSLgAAAACIEEkXAAAAAESIpAsAAAAAIkTSBQAAAAARIukCAAAAgAiRdAEAAABAhEi6AAAAACBCJF0AAAAAECGSLgAAAACIEEkXAAAAAESIpAsAAAAAIkTSBQAAAAARIukCAAAAgAiRdAEAAABAhEi6AAAAACBCJF0AAAAAECGSLgAAAACIEEkXAAAAAESIpAsAAAAAIkTSBQAAAAARIukCAAAAgAiRdAEAAABAhEi6AAAAAGTEK6+8okmTJumWW27JdSh5pTDXAQAAAABIXXl5uaqrq5tNW7NmjSSpuLi4RfmSkhJNnTo1K7FNnDhRK1eu1AMPPKARI0ZowoQJWXnffEfSBQAAAGzj6uvrcx2CJKlLly4J/+7sSLoAAACAbUiiWquysjJJ0qxZs7IdTjOPPPKIbrnlFh100EEaN25cTmPJJyRdAAAAADJixIgRuvvuu3MdRt5hIA0AAAAAiBBJFwAAAABEiKQLAAAAACJE0gUAAAAAESLpAgAAAIAIkXQBAAAAQIRIugAAAAAgQiRdAAAAABAhki4AAAAAiBBJFwAAAABEKGtJl5ldZWbOzIYHr/cwsxfM7B0ze8nM9s5WLAAAAACQLVlJusxslKRDJK2Omfx7SXc65/aU9GtJ92QjFgAAAADIpsiTLjPrJukOSd+X5IJpgySNknR/UGyOpF3MbFjU8QAAAABANmWjpusXku53zq2ImTZEUrVzrl6SnHNOvhZs5/iFzWyKmVWGj5qamiyEDAAAAACZEWnSZWZjJB0k6XcJZrv44onW4Zyb5pwrDR+9e/fOdJgAAAAAEJmoa7rGSfqqpBVmtlJSqaSnJA2XVGpmhZJkZiZf+7U6yXoAAAAAYJsUadLlnJvqnCtxzg1zzg2TVCnpaOfcfZJelTQpKDpR0krn3Moo4wEAAACAbCvM4XtfKOleM7tC0ueSzs5hLAAAAAAQiawmXUFtV/j325LGZPP9AQAAACDbsnZzZAAAAADojEi6AAAAACBCJF0AAAAAECGSLgAAAACIEEkXAAAAAESIpAsAAAAAIkTSBQAAAAARIukCAAAAgAiRdAEAAABAhEi6AAAAACBCJF0AAAAAECGSLgAAAACIEEkXAAAAAESIpAsAAAAAIkTSBQAAAAARIukCAAAAgAiRdAEAAABAhEi6AAAAACBCJF0AAAAAECGSLgAAAACIEEkXAAAAAESIpAsAAAAAIkTSBQAAAAARIukCAAAAgAiRdAEAAABAhEi6AAAAACBCJF0AAAAAECGSLgAAAACIEEkXAAAAAESIpAsAAAAAIkTSBQAAAAARIukCAAAAgAiRdAEAAABAhEi6AAAAACBCJF0AAAAAECGSLgAAAACIEEkXAAAAAESIpCtQWVmpiy66SJWVlbkOBQAAAEAHQtIVmDFjhpYtW6YZM2bkOhQAAAAAHQhJl3wt14IFCyRJCxYsyNvarvXr1+vrX/+6dtllFz3xxBO5DgcAAABACki65Gu5Cgr8rigoKMjb2q7Zs2eroqJCK1eu1BVXXJHrcAAAAACkgKRLUkVFherr6yVJ9fX1qqioyG1ASQwfPlxmJkkaMWJEjqMBAAAAkIrCXAeQD8aPH9+YeBUWFmr8+PG5DimhI444Qs8//7xWrVqliRMn5jocAAAAACmgpkvS5MmT1dDQIElqaGjQ5MmTcxxRcmPHjtUZZ5yhoqKiXIcCAAAAIAUkXZJKS0s1YcIESdKECRNUWlqa44gAAAAAdBQ0LwxMnjxZ1dXVeV3LBQAAAGDbQ9IVKC0t1fTp03MdBgAAAIAOhuaFAAAAABAhki4AAAAAiBBJFwAAAABEiKQLAAAAACJE0gUAAAAAESLpAgAAAIAIkXQBAAAAQIRIugAAAAC0avny5Vq1alXCeatWrdLy5cuzHNG2haQLAAAAQFK333679t57b+2xxx56+umnm82bP3++9thjD+299966/fbbcxRh/iPpAgAAAJDUX//6V0lSXV2dnnrqqWbz5s+fr7q6umbl0BJJFwAAAICkzj//fBUVFWn77bfXmWee2WzemWeeqe23315FRUU6//zzcxRh/ivMdQAAAAAA8tfJJ5+sTz/9VIWFhSoqKmo2b9SoUfroo49UX1+vnj175ijC/EfSBQAAAKBVrSVURUVFLZIxNEfzQgAAAACIEEkXAAAAAESIpAsAAAAAIkTSBQAAAAARIukCAAAAgAiRdAEAAABAhEi6AAAAACBCJF0AAAAAECGSLgAAAACIUGHUb2Bm8yUNltQg6QtJlzjnXjOzlZI2Bw9Jut4596eo4wEAAACAbIo86ZJ0mnPuM0kysxMlzZA0Kph3inNuaRZiyAvV1dWaPn26RowYoVNPPbXF/PXr1+u2227TsGHDdPbZZ+cgQgAAAACZFnnSFSZcgb7yNV45VV5erurq6mbT1qxZI0kqLi5uUb6kpERTp07d6vedOHGiXnzxRUnSggUL9PWvf73Z/HPOOUfz5s2TJPXs2TNhYgYAAABg25KNmi6Z2SxJYYZxTMysB8ysQNI/JP0/59zaBMtOkTQlfN23b99IYqyvr49kvbHWrVuX8O9E09aubbErAAAAAGyDspJ0OefKJMnMzpb0G0n/Jelw59xqM+sq6TpJ9wXT45edJmla+Lq0tNRtbTyJaq3KysokSbNmzdra1Sc1c+ZMXXXVVRoxYoROPvnkFvN/97vf6Sc/+YmGDh2qc889N7I4AAAAAGRPVpKukHPuPjObbmb9nXOrg2l1ZnazpHeyGUsuHHrooXrmmWeSzt9vv/301FNPZTEiAAAAAFGLdMh4M+tjZiUxr0+S9ImkzWbWL6boGZJejTIWAAAAAMiFqGu6+kqaY2Y95AfQWCvpOEnFwfQukkzSB5LKIo4FAAAAALIu0qTLOfdvSaOTzN4/yvcGAAAAgHwQafNCAAAAAOjsSLoAAAAAIEIkXQAAAAAQIZIuAAAAAIgQSRcAAAAARIikCwAAAAAiRNIFAAAAABEi6QIAAACACJF0AQAAAMi6qqoqHXHEERo7dqzeeuutXIcTKZIuAAAAAFn329/+VgsWLNDixYv185//PNfhRIqkCwAAAEDW7brrro1/77bbbjmMJHqFuQ4AAAAAQOdz8cUXa+DAgdq0aZPOOuusXIcTKZIuAAAAAFlnZvr2t7+d6zCyguaFAAAAABAhki4AAAAAiBBJFwAAAABEiKQLAAAAACJE0gUAAAAAESLpAgAAAIAIkXQBAAAAQIRIugAAAAAgQiRdAAAAABAhki4AAAAAiBBJVx6pra3Ve++9p4aGhlyHAgAAALRQV1en9957T/X19bkOZZtSmOsA4NXU1GjMmDFaunSpjj/+eM2dOzfXIQEAACCHysvLVV1dnVLZqqoqSVJZWVnK6y8pKdHUqVNTLl9XV6dx48Zp8eLFOuyww7Rw4UJ16dIl5eU7M5KuPLF06VItXbpUkjRv3jxt3LhRvXr1ynFUAAAAyJXq6mqtWLVarnufNsvaFidJ+mDNZymt2zZ/nnY8q1at0uLFiyVJixYtUmVlpYYOHZr2ejojkq48MWLECO27775asmSJTjrpJBIuAAAAyHXvo037npLx9fZY8lDaywwdOlSHHnqonn/+eY0fP16lpaUZj6ujIunKE7169dLLL7+sDz/8UEOGDMl1OAAAAEAzXbt21cKFC1VZWakhQ4bQtDANJF15pKioiCpaAAAA5K3CwkINGzYs12Fscxi9EAAAAAAiRNIFAAAAABEi6QIAAACACJF0AQAAAECESLoAAAAAIEIkXQAAAAAQIZIuAAAAAIgQSRcAAAAARIikCwAAAAAiRNIFAAAAABEi6QLaYcuWLZo5c6ZmzpypLVu25DocAACASP3tb3/TTTfdpE8++SRpmS1btuiee+7Rfffdp4aGhmbzHnnkEd1+++3auHFj1KHmpcJcBwBsjfLyclVXV7eYvmbNGklScXFxs+klJSWaOnXqVr/v9ddfryuvvFKSVFlZ2fg3AABAR/PCCy/oqKOOknNOjz76qJ599tmE5a655hpde+21kqSPPvpIP/nJTyRJDz30kE499VRJ0uLFi/XAAw9kJ/A8QtKFDqm+vj7S9a9cubLx71WrVkX6XgAAALm0evVqOecktX7eEzsv9lyJ8yaSLmzjktValZWVSZJmzZoVyfteeeWVWrFihZxz+tnPfhbJewAAAOSDiRMn6pxzztGSJUv0y1/+Mmm5q666SqtXr1ZhYaGuuOKKxukXXHCBFi9erMrKSk2bNi0bIecdki6gHYYOHapnnnkm12EAAABErmvXrpo5c2ab5XbddVctXLiwxfQ+ffpozpw5UYS2zWAgDQAAAACIEEkXAAAAAESIpAsAAAAAIkTSBQAAAAARIukCAAAAgAiRdAEAAABAhEi6AAAAACBCJF0AAAAAECGSLgAAAACIEEkXAAAAAESIpAsAAAAAIkTSlWWPPfaYDjvsMP30pz9ts+w999yjww47TDfddFPC+Rs3blRZWZmOPPJIvfHGG5kOFRly6623ar/99tOOO+6owYMH64YbblB9fb0uvvhijRs3Ts8991yuQwQAAECECnMdQGczadIk1dTU6Pnnn9dRRx2lcePGJSy3fv16XXDBBWpoaNDzzz+vE044QbvttluzMtOnT9fs2bMlSZdeeqkWLlwYefy5Ul5erurq6pTLV1VVSZLKyspSKl9SUqKpU6e2K7bWrFy5Uj/84Q+bTSsvL1dRUZHuuOMOSdJ5552nd955J+PvDQAAgPxA0pVlgwYNUk1Njbp06aIBAwYkLde9e3f17dtXn376qXr27Kntttsu4bpCxcXFkcSbL6qrq7Vi1Wq57n1SKm9bnCTpgzWftV128+dbFVtrevfurV69emnjxo2N04qKirTzzjs3vu7onx0AAEBnR9KVZU8//bRmz56tsWPHap999klarmfPnlq4cKEeeeQRHXPMMc0SrNBZZ50l55yqqqr0gx/8IMqw84Lr3keb9j0l4+vtseShjK8zNGDAAC1YsEBz5szR6tWr9Z///Ec/+tGPNGbMGD388MNaunSpLrjggsjeHwAAALlH0pVlu+66q6666qqUyo4cOVIjR45stUyqzeeQO6NHj9bo0aNbTD/ppJN00kkn5SAiAAAAZBMDaQAAAABAhEi6AAAAACBCJF0AAAAAECGSLgAAAACIEEkXAAAAAESIpAsAAAAAIkTSBQAAAAARIukCAAAAgAh16Jsjl5eXq7q6OqWyVVVVktK72XBJSYmmTp3artgAAAAAdA6RJ11mNl/SYEkNkr6QdIlz7jUz20PSfZIGSPpM0jnOuWWZfO/q6mqtWLVarnuftuPc4iRJH6z5LKV12+bPtyo2AAAAAJ1DNmq6TnPOfSZJZnaipBmSRkn6vaQ7nXP3mtkpku6RNCbTb+6699GmfU/J9GrVY8lDGV/nl19+qYaGBvXu3TvlZT755BP169dPXbp0yXg8SGzLli367LPP1L9//7SXdc5p/fr12mGHHWRmEUQHAACAfBN5n64w4Qr0ldRgZoPkE6/7g+lzJO1iZsOijidfLVq0SMXFxRowYID+8pe/pLTMhRdeqAEDBujAAw9UTU1NxBFCkj7//HONGjVKAwYM0A9+8IO0lnXO6cQTT9SAAQN05JFHqr6+PqIoAQAAkE+yMpCGmc0ys39Luk7S2ZKGSKp2ztVLknPOSVotaedsxJOPHnjgAdXU1Ki2tlb33Xdfm+UbGhp01113SZJee+01vfTSS1GHCEmLFy/WkiVLJEl33nlnWst++OGHmjt3riRpwYIFevfddzMeHwAAAPJPVpIu51yZc26IpJ9J+k04Oa5YwrZWZjbFzCrDR0et0fnmN7+pwsJCmZmOP/74NssXFBTouOOOkyQNHTpUI0eOjDpESBo1apRKS0slSSeccEJayxYXF+uQQw6RJI0YMUK77LJLxuMDAABA/snq6IXOufvMbLqkSkmlZlbonKs337lliHxtV/wy0yRNC1+XlpbGJ2sdwvHHH6/3339fdXV12m233VJa5uGHH9abb76pXXbZRX36tD1YCLbewIEDtXTpUq1cuVLDhw9Pa9kuXbqooqJCy5cv15577qnu3btHFCUAAADySaRJl5n1kdTbOVcdvD5J0ieSPpb0qqRJku6VNFHSSufcyijjyXc775xe68rCwkJquHKgb9++7d7v3bp103777ZfhiAAAAJDPoq7p6itpjpn1kB8yfq2k45xzzswulHSvmV0h6XP5vl4AAAAA0KFEmnQ55/4taXSSeW8rgiHiAQAAACCfZGUgDQAAAADorEi6AAAAACBCJF0AAAAAECGSLgAAAACIEEkXAAAAAESIpAsAAAAAIkTSBeSRyspKXXTRRaqsrMx1KAAAAMgQki4gj8yYMUPLli3TjBkzch0KAAAAMiTSmyOjpS+//FIPPvigqqqqNHLkSH3jG9/Qn/70J33xxRf617/+pbKyMu2///56+OGHNXLkSO2000667bbb1NDQoJ122kkTJkzQXnvtpZtvvlmPP/64LrzwQk2cODHXm9WprFixQgsWLNCRRx6poUOHSpI++OADLVy4UN/4xjfUu3dvPfroozrwwAO17777tli+rq5ODz74oD777DP16tVLp5xyinr37q3KykotWLBAkrRgwQJNnjxZpaWlWd02AACAeA0NDfrzn/+sPn366Nhjj202b8uWLXrwwQdVXV2tQYMG6bTTTlO3bt0k+fPeP//5z9pzzz01ZsyYFut97rnntGrVqmbLdFQdOulas2aNbHOteix5KOPrtk2faeXKz3TEEUc0m15XV9fqcm+++abWr1/f+Lp3796qqalpfD1z5szGaV27dlX//v310UcfNSt/7rnn6pZbbpEkPf3005ozZ45OPvnkTGxW3or0s9z8udasqU2p7BdffKFDDjlEH3/8sQYPHqz3339fdXV1Ovjgg7Vu3TqVlJRo0KBBeu2119S9e3e98cYb2n333Zut49JLL9X06dMbX//xj3/Uk08+qRkzZqigoEANDQ0qKCjQjBkz9POf/zyj2woAALYduTj/KS8vV3V1dbNpixYt0sqVKyVJY8aMaXZus3z5cv3zn/9sfP3UU0/p/vvvlySdeeaZeuyxx1RQUKBFixZp7NixjeXmz5+vo48+uvHv2bNnZ2zb8hHNC7Psyy+/bPZ68+bNLcps2rRJkk/gYhMuSaqpqdFLL73UbNrSpUszHCWSWbdunT7++GNJ0kcffaT169dr7dq1WrdunSSpurpay5cvl+Q/2w8++KDFOpYtW9bs9ZtvvilJqqioUH19vSSpvr5eFRUVUW0GAABAymIrCDZs2NBsXngOFIo9zwnPcRoaGvTWW2+1Wa4j69A1XcXFxfpgzWfatO8pGV93jyUPaZfifpo1a1Zayz322GO6+OKLtWHDBu2555665JJLdO2112rdunWqqanR/vvvr+9///v6xS9+odGjR2vkyJH65S9/qS1btqhPnz468cQTdfnll+uQQw7R559/rp133lkXXXRRxrcv30T9WRYX90up7C677KLy8nL94Q9/0FlnndXY/O/HP/6xHnzwQZ199tkqLS3Vr371Kx166KEtakIl6brrrtO5556r9evXq1evXrrxxhslSePHj29MvAoLCzV+/PiMbSMAANj25OL8Z+rUqS2mffOb39Tf//537b333vrTn/6kIUOGNM77xz/+obKyMn388cfq16+fbrjhhsZ5N954oy6//HLttddeOv3005ut85xzztHjjz+uFStWNFumozLnXK5jSEtpaalLdWS3srKySA/UXduRdMELa4sGDRqUUvnO8FlWVlZq0qRJjc0L77///m2mT1e6nyfyF59lx8Fn2XHwWXYc2+r5T1lZmSTl/Fwp35hZlXMupZO1Dl3TBWxLSktLNWHCBP3tb3/ThAkTtpmECwAAbHustkZVVRsbE6rWVFVVSVJKZUMlJSUJa806K5IuII9MnjxZ1dXVmjx5cq5DAQAAHZlrkKvforq177dZtEuDHwYilbKStOZLho2IR9IF5JHS0tJmIxsCAABEpbhng24Y83nG1/uTxX0yvs5tHWkoAAAAAESImi4AAACgs3ENWvNlQSS1Umu+LFDXNWsyvt5tGTVdAAAAABAharoAAACAzsYKVNyzLrI+XV0HFmd8vdsyaroAAAAAIELUdAEAAACdUKp9uj7Z7Otp+ndvSHm93G20OZIuII888cQTmjp1qsrLy3XsscfmOhwAANBRWYGsSxd1HbhTm0W3BDdHTqWsJJXK3xwZTUi6gDwybdo0Oec0bdo0ki4AABAZ1623dirup1mzZrVZtqysTJJSKovE6NOVI08//bROOOEE3XTTTbkOBe1011136fjjj9cjjzyScP6qVat0+umn6/vf/74++OADnXXWWTrooIN0zDHHaOHChS3KP/HEE6qtrZUk1dbW6oknnog0fgAAgKjNmzdPxx9/vC6//HKdcMIJuv7663MdUk6kXNNlZkc7556KMpjOoqGhQRMnTtQXX3yhefPm6Wtf+5pGjx6d67CQhnfffVcXXHCBJGn+/Pn65JNP1Lt372ZlLrvsMj366KOSpJdeekn/+te/Gue9/PLL+uSTT5qVnzZtWovX1HYBAIBsKi8vV3V1dbNpVUHzwrDGK1ZJSYmmTp2acF21tbU67bTTtHnz5sZp8+bN08EHH6wJEyZkMOr8l05N11Vm9raZ/dDMMn8XtU6koKBAvXr1avy7Z8+eOY4I6erevbu6du3a+HdhYcvrF+FnLKlFQhb/WlJjLVey1wAAALlQWFiY8FynLQUFBerRo0eL6YnOgzq6lPeec26smY2SdLGk98zsIUm3O+eWRRZdB/bUU09pxowZGjdunIYPH57rcJCmIUOGaO7cuXryySd1xhlnqHv37i3K3Hbbbdp55521ww476IILLtCNN96ot99+WwMGDND3vve9FuW7devWLNHq1q1bpNsAAAAQL1mtVXt07dpV8+fP1/3336+vfvWrevvtt3XwwQd3yhZeaaWszrlXJE02s/0kzZV0gZktlDTFOfdGFAF2VPvuu69uvvnmXIeBrXDMMcfomGOOSTp/++23169+9avG19dcc02r65syZUqzds5TpkzZ+iABAABy6MADD9SBBx6Y6zByLq2ky8wmyNd0jZR0h6R7JE2Q9Iik3TMeHRDDNn+uHkseSq1sbY0kPzJPKuuV+m1NaBlx7LHHatq0aaqtrVW3bt3ozwUAANBBpDOQxjJJn0i6VdKpzrktwawHzey7UQQHhNK910NV1UZJ0k7FqSRT/fLmXhJTpkzR1KlTqeUCAADoQNKp6Spzzv0z0QznHJfkEal02xdvq/eTOPbYY6nhAgAA6GDSGb3wQjM7zcwGRBYNAAAAAHQw6SRdiyWdKOkNM3vVzH5jZkdFFBcAAAAAdAgpJ13OuRnOuTMllUi6SdIpkp6IKjAAAAAA6AjSGUjjXElHSBol6U1Jv5H0dERxAQAAAECHkM5AGr+X9KKk/5a00Dn3ZTQhAQAAAEDHkU6frv6SfivpWEkvmNlCM/tpNGEBAAAAQMeQTp+uDZL+KmlO8DxMEjcTAgAAAIBWpJx0mdmTkj6S9DNJNZJOlzQworg6hS1btiR87ZxTXV1d49/19fVJ19HQ0CDnXHRBIqGGhoaUy4afpZT8M0d+cc6l9RkDAIC2debznnSaF06TtJNz7gjn3PXOuZecc5yVtMPGjRs1duxYde3aVVdeeaUaGhp06qmnqrCwUEceeaSKiopUVFSkE088UT179lTXrl11wAEHqLa2ttl6nnvuOe2www4aMGCA/vGPf+RoazqfRx99VL1791ZpaanefvvtpOWqq6tVUlKioqIi9evXTzfeeKN69eqlYcOG6fXXX9eBBx6ooqIiXX/99VmMHm159dVXVVxcrH79+umZZ57JdTgAAHQI5eXl6tq1qw4//HBt2rQp1+FkXcoDaTjn5ptZgZmVxC7nnFsdSWQZYps/V48lD7VdrrZGkuS69U55vVK/dsX07LPPavHixZKkX//61yorK9NDD/kYY0/y5s6d21iL9corr2jy5Mnq0qVL4/xFixZpw4YNkqRJkyZpzJgxzd6npKREU6dObVeMSO6WW27Rpk2bVFVVpdmzZ+u6665LWG7OnDn68MMPJUkbNmzQjTfeqNraWq1atUpnnHGGli9fLkm6+uqrG/9es2aNJKm4uLjZujrDZ1leXq7q6uoW07O9T+655x6tXbtWknTHHXfoiCOOyPh7AADQmTQ0NOg3v/mNnHNatGiR/v73v+vII4/MdVhZlU7zwnMkfSbpDUn/Ch7/jCaszCgpKdEuQ3fWrsX92nwUdTEVdbGUyu5a3E+7DN1ZJSUl7Ypr5MiR6t+/vyRpwoQJKi0t1Z577ilJGjx4cGO5nXfeufHvvn37Ni4Tii0bf0KK6EyYMEGS1KVLF40bNy5pubFjx6qw0F+fMDMdeuihkqSuXbtqjz32UFFRkaTmn2N9fX2rzUk7o2zvk/Hjx8vMJDV91gAAoP0KCgo0fvx4SdKgQYM0fPjw3AaUA+kMGX+lpNHOubeiCibT0rkKXlZWJkmaNWtWVOE02mmnnbR06VK99dZbGjt2rIqKivTSSy/p1Vdf1ahRo/Tyyy/rgw8+0He/+129+uqrev311/Wtb31LAwe27EJ33HHHycw0b968yOOGd+WVV+qoo45Sv3799JWvfCVpuQMOOEDvvvuu5s+fr8MOO0x77bWXFi9erIEDB2r33XdXdXW13n33XWRUHOAAACAASURBVH3ta19rTM6yeRzmm2Tf12zvk1NOOUVvvPGGNm/erAMOOCAr7wkAQEf3+OOPa/Hixdp77701aNCgXIeTdekkXWu3pYQr3w0ePLhZDUffvn0brwAcccQRjU2aDjroIB100EFJ17PDDjtEGicSO/jgg1MqN2zYMF1wwQWNr2ObgJaUlLS7thTR2meffXIdAgAAHUq3bt0az3U7ozabF5pZTzPrKelhM7vYzHYIpwXTAQAAAABJpFLTVSPJSbLg9a0xr52kLkmWAwAAAIBOr82kyzmXzrDyAAAAAIAY6fTpApCmZMOgJ1NVVSWpafCItnSGoeQBAAC2dSRdQISqq6u1YvUKKbXbv/kGu5JWrF/RdtmadocFAACALCLpAqLWW2o4uiHjqy14ipa/AAAA2wLO2gAAAAAgQiRdAAAAABAhki4AAAAAiBBJFwAAAABEiIE0/j97dx5fRXX/f/w9N5csbCEJEAhhFXAB2QQRC4LgAopSQFsXhBooAipSxYptxVrxJ1WqlSJfoG2QuCu2iguiLKmyg+wie0K4uRAgIQQSstzc+f0RciUQIIE7meTm9Xw88uDeM2fOfIbJndzPnJlzbLRt2za988476tGjh7xer/r376+wsDBJksvl0tq1a3XzzTcrIiJCkrR27Vqlp6erf//+ysnJUXx8vHbu3KnWrVvbuRvVjmmaeuedd3Tw4EGNHDlSUVFROnbsmObOnau8vDy1bdtWd9xxh+9YFuQXKHNzpk66T+rEnhNq1KuRHMEOHV51WI16NVK9tvUkSdnubOUdzVO9dvXkCOJ6SFWzfPly5ebm6pZbbrE7FJy2fft27dq1S7169dL//vc/derUSa1atbI7LACokjZs2CC326077rhD//3vf/XTTz9p4MCBmj9/vnr37q3Q0FDl5+erX79+pa7/zTffqHbt2rrxxht9ZR6PR1999ZVatGihDh06VNSu2IKkyya7du1Sp06dVFhY6CsbMGCAvvrqKz3xxBOaPXu28vLyFB4errvuuksul0uJiYmSpPbt2+vgwYNKT0+XJO3evbvM8zpJgTW30/nmwTrffFf+2Pfx48drxowZkqQ33nhDSUlJ6tKli5KTk311+vfvr4ULF+rgwYPavGKzck/l+pZl7c7yvc7YnKFrO1wrh8OhLZu3yDRNNWjQQFdedeW5Gz47D/NKaQVpl7Uv8I+EhASNGDFCkjR16lQ988wzNkeEzZs36/rrr1d+fr4iIyOVkZGhunXrauvWrWrWrJml2y7tvJSWliaPx1OudpxOp6Kjo0uUBdL5G0DZGLlZCtsyv2RZ3knJLMfIyIZDZkjJ+WuM3CxJ9cq0+pIlS3TbbbfJ6/Xqxhtv1MqVKyVJzz33nCTppZde8tWdNm2annrqqRLrT5kyxVe3Z8+eatmypSRp5cqV2rt3rwzD0G233aaGDRuWWC+QznkkXTbZtm1biYRLKurJkqQ9e/YoLy9PknT8+HHtObxHqa5UX70DBw8oK+PnL+5ZJ7LKNq+TVG3mdnI6rfvVXrVqle+12+2W2+0ukXBJPx/LoKCgEglXaU5knZDT6ZRpFk3SdeLECRkyStQxDEPOoLP2KUgKDQ29xL2AP61bt873uvjYw15btmxRfn6+JCkjI0OSlJWVpZ07d1qedLndbh1ITlbkGWWFkso7cURhQYGyzzi3ZPghNgBVS0xMTKnlaWl55bqQU3QR5+wEq9552z/b+vXr5fUWncW2b99+wbql/R088+/k0aNHfUnX0aNHJRXdRZSRkXFO0hVISLps0r9/f1199dX66aefFBYWppCQEE2ZMkWSFBkZqQYxDZSRkVF0K9pAhxqkN9DROUflyfaoyX1NFJ4SrpQvU2TIULPBzeTtVbY/54E2t5MdVz+mTJmiwYMHKz8/X6NGjVKLFi30xBNPaPr06ZKkmjVr+o7l119/rYkTJ2rWrFnKzs6WJAUHBysoKEinTp1SvXr1tHbtWjmdTg0cOFA//fST/vGPf+jBBx+s8P3CpRs3bpwWLVqkU6dOaeLEiXaHA0mDBg1Sr169tHXrVt1666365ptv1KtXL/Xu3btCth8pafxZF08u1/Ti2dMBVBuVpZdn+PDh+vDDD+VyufTss8/q+eef14kTJxQdHa3Dhw8rPDxcdevWlWma5/RySdLTTz+tH374QbVr19Znn32mtm3bSpI+/vhjDRs2THXq1NHChQsVFRVV0btWYUi6bFKzZs3zXikwDENXdr6yxIS6oVGh6vxsZ9/7elfWU+ytsZbHiXP1799fp06dKlH297//XX//+99LrT9t2jRNmzbtou2uWLHCL/Gh4l199dXatWuX3WHgDHXr1tV3331ndxgAEBAaN26sDRs2+N7/7ne/K9f6PXv2lMvlOqf83nvv1eeffy5JAZ1wSYxeCAAAAACWIukCAAAAAAuRdAEAAACAhUi6AAAAAMBCDKQBAAAAwG/ON49qac43t+qFVMX5u0i6AAAAAPiN2+1WUkqSVPvidYtnwwj0OWdJugAAAAD4V22VmP7IX6rqnLNVM2oAAAAAqCLo6QJQocpzn7dU/nu9q+J93gAAILCRdAGVyMKFCzV16lRNmjRJAwYMsDscS7jdbiXtT5EZWrdM9Y3Copu996VlXrxubtZlxQYAAGAFS5MuwzBCJX0g6RpJOZIOSRpjmmayYRiJkppJKv6WNM80zdetjAeo7F577TWZpqnXXnstYJMuSTJD6+pUh3v83m7Ylvl+bxMAAOByVcQzXXMkXWmaZidJX5x+X2y8aZqdTv9Ui4QrKytLnTp1Ur169fTUU0+ds3zVqlVasWKFDh045CsrzC3UroRd2vjSRrm+dfnKTa+pAwsPaM97e5SXkVch8eNnhYWF+vOf/6xRo0YpJSVFkjRz5kyNGDFCGzZsOO96Xq9XL730kkaOHKl9+/bpp59+0sMPP6xRo0YpL6/oOObl5WnhwoUVsh/42YwZMzRixAht2rRJkpSUlKSRI0dqypQp8nr9/zAw/Mftdmv06NF67rnnVFBQYHc4AABJx7Yf0+63dyt9c7rdodjO0p4u0zRzJX11RtFqSROs3GZZlPZMyYWeG/HnMyL333+/Nm/eLKmoV+Omm27SoEGDJEmmaWrgwIHKyMiQJNV+q7Zq16mt5L3JOuo6KklK+TxFdZLrKCIyQofch3Rg9wFJUv7OfLXv0P7iAXiltII0v+xLdffvf/9bL7zwgiTpwIED+uMf/6hHH31UkpSYmKj9+/eXut67776rP/3pT5Kk3bt3Kysry/c7cc011ygqKkqSLrm363zPTKWlFR336OjoEuU8A1Vk2bJlevzxxyVJ33//vfbt26e4uDglJiZKkmJjY/Wb3/zGvgBxQePGjdNnn30mSapfv76eeOIJ22JJS0tTvqTpxeMg+0mGpJNpnL8BVA2eAo92/GuHTI+poz8c1XUvXKfg8GC7w7JNRY9eOF7S52e8f9UwjK2GYXxoGEar0lYwDONJwzBcxT8nT1ozOL/T6ZTTaf0jboZhlLlujaAaCg4KVpARVKLc6XAWlTt+LncYDgUHBV/8p0awQkND/bY/sE5xr5e/eDweeTwev7ZZnZTns4uKx/EBAFRmFTaQhmEYf5DURtKY00UPmaZ5wCj6S/moim49vObs9UzTfE3Sa8XvY2NjL/vSoZ1X9d977z316dPHd9tScS+XVPSl4YsvvtDs2bPVp08f31X1EydO6NFHH9X69ev1m9/8Rr///e8lFd3edt111yk7O1tLly5V06ZN7dilamvkyJE6dOiQXC6XnnvuOTVt2lQzZ87UmjVrLniV/cEHH9SBAwe0b98+/fGPf1ReXp5eeeUVLV++3NfLJUkhISGXFNf5fr+Le3ETEhIuqd1Ad/PNN+vNN9/U2rVr9bvf/U6SNHfuXE2ZMkUtW7bUQw89ZHOEuJCZM2cqOjpaDRs21Lhx42yNJTo6WtnJyRov/yaC02Wq1lk91QBQWTlrOHXVqKt0dMNRRXaIrNa9XFIFJV2GYUyUNETSLaZp5kiSaZoHTv9rSpphGMY0wzCiTNMM6Js+69ate8HnfXr06KEePXqUKKtTp06pX5SDgoLUoUMHSSLhskFQUJAmT55comzs2LEaO3bsBddzOBz6wx/+UKIsPj5eCxcu1Msvv+wre/LJJ/0XLMpk3LhxJb6wt2jRQv/6179sjAhl1bhxY82aNcvuMAAAZ4i4JkIR10TYHUalYPnthYZhPCnpfkm3mqaZebrMaRhG9Bl1hkpKC/SEC7iQAQMG+Hq3QkJCAnr0QgAAgOrE0qTLMIxYSX+TVE/SMsMwNhmGsUZSiKQvTz/PtVnSOEl3WxkLUBU8+eSTMgyDXi4AAIAAYvXohS7pvDe1d7Vy20BVNGDAAHq4AAAAAkxFj14IAAAAANVKhY1eCMC/zjcf1/lcaC660lg1f1daWpqM3DyFbZnv97aN3CylpTFROAAAqFxIuoAqyu1260BysiLLWL/4Pt/s5OSL1s241KAAAABwDpIuoAqLlPw+F5BUNB+QVaKjo7UvLVOnOtzj97bDtsxXdHQ9v7cLAABwOXimCwAAAAAsRNIFAAAAABYi6QIqEZfLpTFjxsjlctkdCgAAAPyEpAuoROLj47V9+3bFx8fbHQoAAAD8hKSrEtq7d6+uvvpqNW7cWImJiZKk//znP6pfv766du2qw4cP2xsg/ObMY/3xxx9r6dKlkqSlS5fS22WDDz74QJGRkbrhhhuUnp5udziwwIsvvqi6detq8ODB8ng8doeDUuzYsUNt2rRRbGysVq5cKUnKy8vTwIEDFR4erldfffWC6x87dkw33nijwsPDFR0draZNm2rNmjUVETpQLSxYsEANGjRQ586d9dJLLyk8PFy33nqrXnzxRYWHh2vQoEHyer0XbCPlyxSteXqNds7dKdNr3eBdlQmjF1ZCc+bM0Y4dOyRJf/3rX9WnTx+9+OKLSk9PV3p6uu644w5dc801ki4895JV8yzBf8481n/4wx/UtGlTeb1eORwOxcfHa/LkyeddNy0tTfmyZqTBDEkn09L83m5l98ILL+jYsWNas2aNPvroI40dO9bukOBHHo9Hzz//vEzT1KeffqqVK1fqpptusmRbGSrbZ/P46X/Dy9hmrcuI6WImTZqkdevWlSgrKCi4pLZq1KhR4n23bt3K/Pdo5syZ2rNnjyRp2rRp+s9//qPExER9+eWXkqQ//elPevrpp8+7/vz587Vq1SpJUlZWliTp17/+tW666SalnT6vRUdHn7MefzOBspkyZYqOHj2qo0ePaseOHcrNzdXixYu1dOlSeb1eLViwQNddd53CaobJ8clZ/TteqbCwUK6VRReW0zemK8ebozp165x/g2d3EXmltIKq9x2FpKsS6tatm+/19ddf7/t306ZNMgxDUVFRvuVOJ4ewKjvzWBcUFPiuvHs8HiUmJl4w6YL/XX/99dqxY4ecTqe6dOlidzjws+Lj+sMPP6hevXpq06aNJduJiYkpc93M0xfOajVpctG6tcrZdlVV/HfvzNdXX3216tSpoxMnTpQ4b5amc+fOcjgcJa60F//dpHcTuHzXX3+91q1bJ4fDoXbt2umHH35QzZo11aZNG23evFnh4eGKjIxUYWHhOet6TI/kkGrWrKmcnBw5nU6FhobKOM/0N4ZhyBl01nfdICk0NNSKXbMU39groXvuuUerV6/W8ePHddttt0kquvJ31113qWnTpurYsaPNEcJfio91Zmam1qxZo8TERHk8HjmdTvXp0+eC60ZHRys7OdmyebpqlXIlOND9+9//1tChQ9WqVSu1b9/e7nBggaVLl2rJkiXq0qWLGjdubMk2ytNbUnyXQkJCgiWxlEdl6eUZNmyYWrVqpZycHN1yyy2SpGbNmmnTpk3avHmzbr311guu37VrV23ZskV79+5V3bp15fV61bdvX0mV6/8bqKqmT5+uAQMGKCYmRldddZUWLVqkdu3aqVGjRlq8eLE6d+6sFi1aXLCNzMxMLVu2TF27dlXTpk0rJnCbkXRVUt27dy/xPigoSAMHDrQpGlip+Fi3a9fO90yX1+tVXFycnWFVS06nU3fffbfdYcBCxc9zoXK78cYbzylr1aqVWrVqVab127Vrp3bt2vk7LACSHA6H7rzzTt/7X/7yl77XZT2/1qtXr9qdi0m6gEoiNjZWffv21eLFi9W3b1/FxsbaHRIAVHqTJk2S2+0uU90LPQddGp7zAuAvJF1AJRIXFye3200vFwCUkdvtVlJKklS7DJVPj22SlJF08bonLyssACiBpAuoRGJjYzVr1iy7wwCAqqW25L39wkNUl5djEbPqAPAfzigAAAAAYCF6uoAqrKxzAUmVaz4gAACA6oSkC6iiyjtfD/MBAQAA2IOkC6iiyjuiFvPTAAAA2INnugAAAADAQiRdVZzL5dKYMWPkcrnsDgUAAABAKUi6qrj4+Hht375d8fHxdocCAAAAoBQkXTbKzc3V0qVLdfjw4Uta3+VyaenSpZKkpUuX0ttVxZw6dUrLli3TkSNH7A4FZ0hPT9fSpUuVnZ1tdyjwE6/Xq++++07Jycl2h4Jy2LJlizZs2HBZbZw6ckpZe7P8FBEAfzNNU99//72SksowYXkVx0AaNrr99tv13XffKTo6Wlu2bFHDhg3LtX58fLwcDoe8Xq8cDofi4+M1efJki6KFv91yyy1auXKlGjdurK1btyoqKsov7U6aNElut/uc8tTToxcWD6hRLCYmptyDcgSq48ePq3Pnzjpw4ICuu+46rVu3ToZh2B0WLtPYsWM1Z84chYaGavXq1erYsWOFx1Dez6VUvT+b7733noYNGybTNDV79myNHj263G2cSDqhbdO3ySw0FdM3Ri1+2cL/gQK4LI899phmzpyp0NBQrVixQl26dLnktko7z6alpUmSoqOjz6lf0edYerps4vF49P3330sq+oXYvn17udtITEyUx+PxtZeYmOjPEGGh3NxcrVy5UpJ08OBB7dixw/JtOp1OOZ1cZ7mQPXv26MCBA5KkH374QVlZXCEPBMuWLZNU9LlbtWqVzdGUxOeydMuWLZNpmr7XlyJrX5bMwqI2ju86fpHaAOxw5vm5+HuRP3k8Ht93ZbtxpreJ0+nUU089pb/97W/q3bu3brjhhnK30adPH1/i5XQ61adPH/8HCkuEhoZqwoQJeuONN9SvXz9169bNb21X1yvj/tCxY0cNGDBAX3/9tR555BGFh5dlKmlUdhMnTtRjjz2mK664QoMHD7YlBj6X5TN69Gh9+umnKigo0NixYy+pjfqd6+vQ8kPKP56vmJuZdxCojCZOnKgxY8aoZcuWGjp06GW1Vdp5tjJNl0PSZaNXX31VU6dOVVBQ0CWtHxcX53umy+v1Ki4uzp/hwWKvv/66pk2bdsnHH/7ndDr11VdfqbCwkOMSQEaPHq2RI0dyTKuQbt26+Z53vtRbfEMiQ3Td89fJ9JoyHNwmDFRGcXFxGjFiRLU4P3N7oc0u55csNjZWffv2lST17dtXsbGx/goLFaQ6nGSqIo5L4OGYVj2GYfjlmUoSLqByqy7nZ3q6qri4uDi53W56uQAAAIBKiqSriouNjdWsWbPsDgMAAADAeXB7IQAAAABYiJ4uAABQZaWlpUl5kmORn68jn5TSCtL82yaAaoueLgAAAACwED1dAACgyoqOjlZSRpK8t3v92q5jkUPRkdF+bRNA9UVPFwAAAABYiJ4uABXOyM1S2Jb5Zaubd1KSZIbULlO7Ur3LCQ0AAMDvSLoAVKiYmJhy1U9NzZYkNYkuSzJVr9ztAwAAWI2kC0CFmjp1arnqDx8+XJKUkJBgRTgAAACWI+kCgMuQl5enadOmKTc3V88884xq1774bZAALt2GDRs0b9489evXT1lZWVq3bp1qNaqlEIWcUzdja4aO7zquBt0aqHYzPptV2ddff62FCxfq/vvv1w033GB3OJD01VdfadGiRRo2bJgOHTqkxYsX66GHHlLXrl3tDq1SIukCgMtwyy23aPny5ZKkjz/+WNdff72k03MHqWhktTPFxMSUu7cPQBGv16vbbrtN6enpmjFjhrzeohELa6bUVJdaXUrUPZV9Sjv+t0MypSMrj6j7rd1lnDKKFtYsw8ZOSor0b/x2mjRpktxu9znl5ztXSZXnfJWSkqK77rpLHo9Hb731ltLS0hQaGmp3WNVaUlKSBg0aJI/Ho7lz5yonJ0eFhYV6++23dfjwYTmd1qUYgwYN0smTJ8tUt6CgQJLUr1+/Mrdfu3ZtffbZZ5cU24WQdAHAZcjPz/e9Lj65S5LH47EjHCCgeb1e5eTk+F4XMwxDLSNblqh7zDgmmUWvTa+pFhEtdDD3oCSpSWSTi28ssvzPoFZFVeFclZeX54szNze3SsQc6M48Drm5uSosLJQknTp1qsRn06ptFxQUKKgMdU9fZpH3jL/PF1J4un0rkHQBwGVYtGiRnn76aeXm5urVV19Vo0aNJPEsGmAFp9OpTz75RLNmzdKtt96qY8eO6YcfftAzzzyjHj16nFN/5syZWrRokR555BHdcccd1fpzeb4eq6rwf9KmTRv985//1Oeff66HH36Y27grgauvvlqzZs3SV199pVGjRunAgQNatGiRRo8ereDgYEu3HR0drezkZI33pVT+M12mapXS6+sPJF0AcBnq1aunf/7zn3aHAVQbAwYM0IABA8pUd9y4cRo3bpzFEaEijBo1SqNGjbI7DJzhkUce0SOPPOJ7z2ftwpgcGQAAAAAsRNIFAAAAABYi6QIAAAAAC5F0AQAAAICFSLoAAAAAwEIkXQAAAABgIZIuAAAAALAQSRcAAAAAWIikCwAAAAAsRNIFAAAAABYi6QIAAAAAC5F0AQCAKmPfvn3q1q2brr32Wm3ZssXucIBqKT8/X/fee69atGihd955x+5wqgSn3QEAAACU1Wuvvab169dLkl566SV9+OGH59SZNGmS3G73OeWpqamSpOHDh5+zLCYmRlOnTvVztPY43/6X5kL/J6UJpP8nXLovv/xS8+fPlyRNmDBBw4YNq9DtZ0iaLvOi9Y6f/je8HO3WusSYLoakCwAAVBnt2rUr9XVZOJ3V42uP2+1W0v4UmaF1L1rXKCz64rovLfPidXOzLjs2BIY2bdqoRo0aKigoKPfn8HLFxMSUuW7m6YsKtZo0KVP9WuVsvzyqx9kHAAAEhLFjx6pp06bKy8vTkCFDSq1DT4xkhtbVqQ73+LXNsC3z/doeqq727dtr9erV2rJly3k/h1Ypz+e7uAc3ISHBqnDKjKQLAABUKQMHDrQ7BKDa69Kli7p06WJ3GFUGA2kAAAAAgIVIugAAAADAQiRdAAAAAGAhki4AAAAAsBBJFwAAAABYiKQLAAAAACxE0gUAAAAAFiLpAgAAAAALkXQBAAAAgIUsTboMwwg1DONTwzB2GYaxyTCMrw3DaHF6WcPT73cbhrHNMIyeVsYCAAAAAHaoiJ6uOZKuNE2zk6QvTr+XpKmSVpum2UbSw5LeNQzDWQHxAEAJBw8e1I8//qjMzExt2rRJhYWFkqRdu3bpwIEDkqTDhw/r/fffV1ZWVqltnDx5Uhs3btSPP/6olJSUCosdACq71NRU/fTTT+eUnzhxQhs3blRBQcEF19+5c6dcLpdV4eEy/PTTT3K73XaHUSVYmuSYppkr6aszilZLmnD69a8ktTxdb51hGGmSekpKtDImAJXTpEmTSj1xp6amSpKGDx9eojwmJkZTp0697O2uX79evXv3Vk5OjurUqaMTJ07o3nvv1c0336xx48apRo0aeu+99/Tggw8qPz9fderUUXp6umrUqOFr4+TJk+ratat27twpSQoKClLv3r3VpEmTy4rtfP8naWlpkqTo6OgS5f76PwFQtaWlpck4dUo118b/XGh6L60x44zr86ZXaWl55Vp9xYoVuuWWW5Sbm6tXXnlFTz/9tCQpKytL1113nfbs2aO+fftqyZIlpa7/j3/8Q+PHj5fD4VC/fv3UqFEjSec/D0qcCyvKK6+8omeeeUahoaFasmSJbrzxRlvjKe1v5vm+Q0gV/3tS0c90jZf0uWEYUZIcpmkeOWNZsqRmZ69gGMaThmG4in9OnjxZQaECqAycTqecTuuuDy1ZskQ5OTmSiq66StKCBQv0+eefS5IKCgo0b9485efn++rs3r27RBs7d+70JVySVFhY6DvRW8Hj8cjj8VjWPoCqLTQ0VME1aijYGeT7cRiGDKlcPw7DKNFGcI0aCg0NLVcs33zzjXJzcyXJd16VinpI9uzZI0launSpsrOzS11/wYIFkiSv11viCzXnQfsVH5vc3Fx9++23NkdTOqu/Q5RHhUVhGMYfJLWRNEZSmCTz7CqlrWea5muSXit+Hxsbe/Z6AAKAXVclBw8erNdff11HjhxRo0aN5Ha7NWbMGPXq1UuLFy9WzZo1NXHiRC1fvlyZmZmKiYnRVVddVaKN9u3bq2fPnlq+fLkcDodq166tli1bXnZs5/s/Kb5il5CQcNnbABB4PvvsM7tD8Ln33nv15ptvKjMzU6NGjfKVd+zYUTfccINWr16tYcOGqVatWqWuP3LkSCUmJqpOnTp6++231blzZ0mcByuD3/72t1q9erUiIiI0dOhQu8Op9L2bFZJ0GYYxUdIQSbeYppkjKccwDBmG0eCM3q7mkngQAkCFatu2rVJSUpSbm6tatWopKytLERERkqSMjAw5nU6FhoYqPT1dSUlJatWqlQyj5DWikJAQfffddzp27JiCg4PldDo1evRoO3YHACqV9u3bKzU11Xd7drHQ0FCtWLFCmZmZioyMPO/69913n+68804FBwcrJCSkIkJGGY0YMUJDhgzh2JSR5UmXYRhPSrpfRQlX5hmLPpb0qKQ/G4bRTVIjScutjgcAzhYcHKzg4GBJqvuVTgAAIABJREFU8iVcklS7dm3fa4fDoSuuuOK8bRiGccEvDgBQXYWEhJT6pdzhcJTpvHlmsobKhWNTdpYmXYZhxEr6m6R9kpadvjqcZ5pmd0nPSHrbMIzdkvIlPWSaJjfnAgAAAAgoVo9e6NL5n9VKk3SbldsHAAAAALtV9OiFAAAAAFCtkHQBAAAAgIVIugAAAADAQiRdAAAAAGAhki4AAAAAsFCFTI4MAFXdpEmT5Ha7y1w/NTVVkjR8+PAy1Y+JidHUqVMvKTYAAFC5kXQBQBm43W4lpSRJtS9eV5JkFv2TlJF08bonLzksAABQBZB0AUBZ1Za8t3v93qxjEXd6AwAQyEi6AFRrXq9X//rXv3T8+HGNHDlSc+fOVVhYmB555BEFBQVp2bJlSkxM1PHjx3U49bByF+WqUa9GctYsOn2ahaZSl6XqyPojcjgcqnNFHQXVCFKdVnUU2T7S5r0DAHu9/fbbSklJ0aOPPqqUlBR98skn6t+/v3r06FFq/d27d+udd95R79695Xa7tX//fo0bN04REREVHDngXyRdAKq1N998U+PHj5ckJSQkaNu2bZKk7OxsDR48WLfddps8Ho+cTqc8Ho+0STq57qSu6XaNJClld4pSdqX42st2Zfted7yxo+pE1Ll4ECeltII0P+4VANjvww8/9D3XunbtWn3//fc6duyYXn31VSUlJSk6Ovqcdfr27SuXy6WgoCAVFhZKktatW6dPP/3UkhhLe143La3ofFxafDx/i0vFPS0AqrXDhw/7XmdmZvpep6Wl6dixY0WJluT74y9JBfkFP7/O+/n12c6sBwDVzZnn10OHDun48eOSpFOnTikrK+uc+oWFhTp69KjvdWntVASPx+M79wP+Qk8XgGrtySef1O7du3X8+HE988wzevXVV1WzZk1NmjRJ9evX1wsvvKDFixfLNE3tTt6t3Jq5aj6oubxNi57tatKjibLfytaJpBOSIYVEhCgoJEh129RV+KBweR0XfwbMscih6Mhzr6gCQFU2atQobdy4USkpKZo2bZo2btyouXPnavDgwWrTps059YOCgvTOO+/ojTfe0E033eS7vXDatGmWxVhar1Vx71xCQoJl20X1Q9IFoFqLiIjQBx984Hvfp0+fEssnT56syZMnF/0RDj93II3gusFqP759RYQKAFVKWFiY4uPjfe87deqkhx9++ILrDB06VEOHDrU6NKDCkXQBAADAduWZD5G5EFHVkHQBAADAduWaD5G5EFHFkHQBAACgcrBgPkTmQkRlwG8hAAAAAFiIpAsAAAAALETSBQAAAAAWIukCAAAAAAuRdAEAAACAhUi6AAAAAMBCJF0AAAAAYCGSLgAAAACwEJMjA6i2fvjhB40bN06NGjXSvHnzVK9evRLLN2/erDFjxigsLEzbtm1TvjdfV3S7QqGRoSXq7Zy7U+mb0mU4DQWFBMlb4FXTO5qqyc1NKnJ3ACAgHN9zXMn/TVZo/VC1frC1goKDSiw/uvGoXN+4VKdFHbW6t5UMh2FTpEDZkXQBqLYmTpyotWvXSpL+7//+T88++2yJ5ZMmTdLq1atLlKV+k6or7rvC996T41H6xnRJkllgylPgkSTt/+9+Nb6psRxBZbihIEdKPZWq4cOHlynu1NRUSSpz/ZiYGE2dOrVMdQHAbkmfJCknNUfZB7IV3iZcjXo2KrF87wd7VXiqUDmpOYq8NlIR10TYFClQdiRdAKqtFi1a+F43b978gsuLhUSGlHjvCHZIhiSzZD1nTWfZEi5J8kpeb4Gyk5PLVL34mm5Z6meULQIAqDRCI0OVk5oj6dxzbnFZTmqO5JCC6wVXdHjAJSHpAlBtzZw5U926dVOjRo00ZMiQc5b//e9/V4cOHRQREaHp06fraM5R1b+lfok6DqdD7R5vp+T/JissOkzBdYOVfzxfzQY2K1cskZLGy/+3yEw/OxsEgEquzUNtdHjNYYXUDym1F+uasdfo6Pqjqt28tmrF1LIhQqD8SLoAVFthYWEaN27ceZeHhIRo7NixkqSvvvpKQRlB8jq859QLbx2ujk93tCxOAKhOgkKD1Lh34/MuD64brJi+MRUYEXD5GL0QAAAAACxE0gUAAAAAFiLpAgAAAAALkXQBAAAAgIUYSAMAAAC2S0tLk/IkxyI/9wmclNIK0vzbJlBO9HQBAAAAgIXo6QIAAIDtoqOjlZSRJO/t507NcTkcixyKjoz2a5tAedHTBQAAAAAWIukCAAAAAAuRdAEAgGrB5XJpzJgxcrlcdocCoJoh6QJQbbndbq1fv14ul0snT56UJBUWFio5OVmHDh3Stm3blJmZWWKd/Kx85WXmKXNnpo7vPa6M7Rk6uumovAVeFeQU6NCKQ8o7nmfH7gABJS8vT8nJyTJN029txsfHa/v27YqPj/dbm7g4j8ej1atX69ixY1qyZIm2bNmi/fv3Kzc397zr5B8vOtdmJWUpJy1H3oKi57zyMvJUmF9YUaEDfsNAGgCqpc8//1yDBg3yfaFr0KCBli9frscee0zffvutr15oaKgSExMlSYdTD2vX5F1SKc94GzUMmQWnvxx+KLUf3151W9e1ejeAgJSZmanu3btr165dGj58uObNm3fZbbpcLi1dulSStHTpUsXFxSk2Nvay28WFFRYW6sorr9S+ffvOWXbFFVdozZo1ioqKKlF+8LuDSpqfVKIsJCpE9a6up7TlaQoOD9a1T12rkHohlsYO+BNJF4Bq6V//+leJK+hHjhzRJ598UiLhkqTc3Fx9+umnRXVSjpSacEn6OeE6Le2DNNW7ut65FR2Sap5V5pUyJE2X/67oF8uQdDKN+WlQtaxdu1a7du2SJL377rt66623ZBjGZbUZHx8vh8Mhr9crh8Oh+Ph4TZ482R/h4gIOHDhQasIlSXv37tWqVas0cODAnwtPSkdXHD2nbl56no6uKirPP56vE++dUFh0WOkbPfs8e3qeruHDh5cp5tTUVEkqc/2YmBhNnTq1THVRfZF0AaiW4uLi9Pnnn/sSr6ioKA0ZMkRLlizRkiVLfPVCQkJ0991367PPPtM1V1+jFStWlNqeYRglkrjYJrEKDgo+p57T6Txn6OL9J/ZLfryFCqjqunXrptatW2vPnj267777LjvhkqTExER5PB5JRbe7JSYmknRVgNjYWLVo0ULJycnnLGvZsqVuuOEG3/uYmBhJUm52rtavX1+ibmhoqCIjI+V2uxUcHKwGUQ1KPcdKpZxnI4smXj6QnKzIMsRc/NuWXUrMZ8soQ3uARNIFoJoaNGiQUlJS5HK51LhxY0VGRqpOnTpatGiR9u/fr7CwMB0+fFjNmjVTRESEevToIUk6ePCgPB6Ptm/fruDgYGVnZys7O1u//OUvlZ2drffee09DhgzxfXkoi+HDhys7OVnjdflfLM82XaZqRTM/DaqWiIgIbd26VW63Wy1btvRLm3369PElXk6nU3369PFLu7gwp9Op3bt3a9WqVWrXrp3WrVunBg0aqF69emrcuLHCwn7urTqzt8jlcsnr9So5OVn169dXy5YtFRYWpqSkJDVs2FC1atUqVxxWnWetuEMBgYmkC0C1FRsbe84zHUFBQWrVqpUkqXHjxuesU1zWtGnTc5aFhITosccesyBSoPoJDQ31fRb9IS4uzvdMl9frVVxcnN/axoU5nU716tVLknT77beXaZ3ic3OzZs1KlPsrCQcqGqMXAgCAgBcbG6u+fftKkvr27csgGgAqFD1dAACgWoiLi5Pb7aaXC0CFI+kCAADVQmxsrGbNmmV3GACqIW4vBAAAAAALkXQBAAAAgIVIugAAAADAQiRdAAAAAGAhki4AAAAAsBBJFwAAAABYiKQLAAAAACzEPF0AUE6LFi3S7t27NWLECNWpU0eS5PF4lJCQoNDQUN1///0yDMPmKAHAft9884127dql4cOHq27dupKkffv2acGCBerbt686dOhw3nU/+eQTHT16VO3bt9f333+vgoICde/eXbfddltFhQ/4DUkXAJTDt99+q/79+0sq+jKxYMECSdLkyZP18ssvS5IOHz6sCRMm2BYjAFQGS5cu1e233y5J+vrrr/XFF18oPz9fPXv21MGDB1W7dm3t2bNH0dHR56ybkJCgESNGSJIcDoe8Xq9v2ZIlS9S3b99yxZKWlqZ8SdNlXvoOlSJD0sm0NL+2icDE7YUAUA5JSUm+1/v27bvoawCorko7X546dUqHDh2SJJ08eVJHjhy56LpnJlxnLwOqCnq6AKAchg0bpsWLF2v37t3629/+5it/4YUX5HK5FBISot///vc2RggAlcMDDzygb7/9Vjt37tS0adMkSeHh4XrjjTc0Z84c3X333Wrfvn2p6z766KNau3atjhw5otatW2vVqlUqLCzUjTfeqAceeKDcsURHRys7OVnj5d9bv6fLVK1SeuqAs5F0AUA51KxZUx999NE55VdeeaWWL19uQ0QAUDmFhYXpgw8+OKf88ccf1+OPP37BdevXr68vv/zSqtCACsfthQAAAABgIZIuAAAAALAQSRcAAAAAWIikCwAAAAAsRNIFAAAAABYi6QIAAAAAC5F0AQAAAICFSLoAAAAAwEJMjgwAlUCGpOkyy1T3+Ol/w8vYbq1LjAkAAPgHSRcA2CwmJqZc9TNTUyVJtZo0uWjdWpfQPgAA8C/Lky7DMKZLultSc0nXmqa57XR5sqTc0z+S9LJpmh9aHQ8AVDZTp04tV/3hw4dLkhISEqwIBwAA+FlFPNM1X1JPSftLWXaPaZqdTv+QcAGoMKZp6tlnn9XNN9+sWbNm6brrrlPjxo01ceJEeb1e/f73v1ffvn21ZMmSi7aVlpama6+9VvXr19fMmTMrIHoAqNqWL1+ufv366YknnlBhYaGvfNu2bWrXrp0aN26sGTNm+MpzcnI0cuRI3X777dq6dasdIQOXxfKeLtM0v5MkwzCs3hQAlNk333zj62Favny5PB6PJOlvf/ub6tatq1dffVWStGPHDrnd7gu2NWHCBG3btk2S9Pjjj+vhhx9WWFiYhdEDQNUWFxen3bt3a+nSpfrFL36hX/3qV5Kk0aNHa/v27ZKKzqf33HOPGjVqpDlz5ig+Pl6S9Nhjj+l///tfubdZ1mdneW4WVrD7ma53DcNwSFoj6VnTNI+cXcEwjCclPVn8Pjy8LB8BALiw+vXryzAMmaapsLAwnThxwresefPmvmUNGjS4aFuNGjXyvQ4NDVWNGjUsiRkAAkWDBg20e/du3+tiDRs29L0ODQ31XcA6s05ZzstnK8+zrTw3CyvYmXTdZJpmimEYNSRNkTRP0h1nVzJN8zVJrxW/j42NLdvwXgBwAdddd50WLFigtWvXaujQoUpISNCePXv029/+VgMHDlRERITWr1+vUaNGXbStV155RR6PRzt27NArr7wip9Pu61kAULnNnz9fs2fPVvv27XXzzTf7yufNm6dnnnlGqampev75530X2x988EEVFBTI5XLpscceK/f2yvPsLM/NwgqGaVZMDnN64IyBxQNpnLWssaRdpmnWuVg7sbGxpsvlsiBCVKTDhw9LKnlFC1UXx7NiWfmFgGMZODiWgYNjWbE4x6KsDMNINU0ztix1bZkc2TCMWoZh1Duj6H5JG+2IBQAAAACsVBFDxr8paZCkRpIWG4ZxUtJtkj4xDCNIkiFpn6ThVscCAAAAABWtIkYvfFTSo6Us6mz1tgEAAADAbrbcXggAAAAA1QVJFwAAAABYiKQLAAAAACxE0gUAAAAAFiLpAgAAAAALkXQBAAAAgIVIugAAAADAQiRdAHCZTp48KY/HI0nyeDw6ceKEzREBAIDKhKQLAC7DrFmzFB4erubNmysxMVEtWrRQeHi43nzzTbtDAwAAlQRJFwBchlmzZsnr9crtduv1119XamqqTNPUrFmz7A4NAABUEiRdAHAZBg4cKEmqWbOmHnjgAdWqVatEOQAAgNPuAACgKpsyZYp+9atfqUGDBmrcuLF69+6ttLQ0dezY0e7QAABAJRFwSZfX65VpmnaHYSnDMORw0EkJVBYdOnTwvW7UqJEaNWpkYzQAAKCyCZikKz8/XykpKSooKLA7lApRo0YNNWvWTMHBwXaHAgAAAOACAibpSklJUZ06dRQVFSXDMOwOx1KmaSo9PV0pKSlq3bq13eEAAAAAuICASLq8Xq8KCgoUFRUlpzMgdumioqKilJGRIa/Xy62GAAAAQCUWEN/Wi5/hCvQerjMV72ugP78GAAAAVHUBkXQBAAAAQGUV8EmXx+PRX/7yF1111VVq166drrrqKo0ePVqZmZnnXec3v/mNZsyYIalo4tPXX3+93NtNTExU165dLzluAAAAAIEh4B+AGjlypDIyMrRq1SpFRETI6/Xqk08+UUZGhurVq3fR9ceMGVMBUf6ssLBQQUFBFbpNAAAAANYJ6J6uPXv26OOPP9bcuXMVEREhSXI4HLr33nv1+OOP6/333/fVXbRokbp3735OG3/+8581ceJESdJbb72l22+/Xffff7+uvfZade3aVfv27fPV/dOf/qTWrVurd+/e+uKLL0q08/bbb6t79+7q0qWLevfurW3btvna7N+/v4YPH66uXbtq7dq1mjJliq6++mp16tRJnTp10v79+/3+fwMAAACgYgR0T9eGDRvUpk0b1a9f/5xlEyZM0AsvvKD7779fkjRjxgw99thjF21zzZo12rx5s5o3b65Jkybpr3/9q2bPnq3PP/9cCxYs0KZNmxQWFqbBgwf71lmxYoU++OADfffddwoJCdH333+vBx98UJs3b5YkLV++XBs3blSbNm107NgxDRgwQAcPHlRYWJhycnIYnRAAAACowqrtt/lbb71Vx44d0+bNm5WUlKT169frV7/61UXX69mzp5o3by5J6tGjh/bu3StJWrZsmX7961+rdu3aCgoKUlxcnG+dzz77TJs3b1b37t3VqVMnPf744zpy5Ijy8/N9bbZp00aSVLduXbVp00bDhg3T7NmzlZGRodDQUH/vPgAAAIAKEtA9XV26dNHu3buVnp6uqKioc5aPHz9eb775psLDwxUXF6eQkJCLtnlmAhQUFCSPxyPpwkO3m6apuLg4/eUvfyl1ee3atUu0uXr1aq1cuVKJiYm64YYb9P7776tXr14XjQ0AAABA5RPQPV2tW7fW0KFDNXLkSN9ohaZpKiEhQXv37tVDDz2khQsXat68eZc9YEa/fv300UcfKTs7W4WFhXrrrbd8y+666y4lJCTowIEDkoomc16/fn2p7Zw4cUJpaWnq1auXnnvuOfXs2VMbN268rNgAAAAA2Cege7okKT4+XlOmTFH37t3ldDplmqZuuukm3X333apZs6Z++ctfyu12q2nTppe1nYEDB2rVqlXq2LGjmjRpot69e8vlckmSbrrpJv2///f/NGjQIBUWFqqgoEB33nlnqUPKHz9+XPfcc4+ys7NlGIbatGmjESNGXFZsAAAAAOxjXOi2uMooNjbWLE5mihUWFmrXrl1q27ZtuYZbLywsVJcuXTRjxowqd/vepe5zZXH48GFJUsOGDW2OBP7A8axYw4cPlyQlJCT4vW2OZeDgWAYOjmXF4hyLsjIMI9U0zdiy1A3o2wsvZMGCBWrVqpVuvPHGKpdwAQAAAKg6Av72wvO5++67dffdd9sdBgAAAIAAV217ugAAAACgIpB0AQAAAICFSLoAAAAAwEIkXQAAAABgoWo5kMbDvx2jI+npfm+3QVSU5v5zlt/bBQAAAFB1Vcuk60h6ug5ecZf/G977eblXeeGFF/TnP/9ZW7duVfv27f0fEwAAAABbcXuhjTZs2KDVq1erWbNmdocCAAAAwCIkXTbJy8vTo48+qpkzZ8owDLvDAQAAAGARki6bTJ48WcOGDVPLli3tDgUAAACAhUi6bLBq1SqtW7dO48aNszsUAAAAABYj6bLB//73P+3YsUMtW7ZUixYt5HK5dPvtt2vhwoV2hwZUK/PmzdPgwYP13//+t1zrHTp0SMOHD9fYsWN14sQJi6IDgMBnmqaef/553XPPPdq0aZMkad++fbrvvvv05JNPKi8vT5Lk9Xo1efJk3XPPPdq8ebOdIQOXpFqOXmi3SZMmadKkSb73LVq00BdffMHohUAFSk5O1sMPPyzTNPXll1/qyJEjCg8PL9O6EydO1LvvvitJioqK0pQpUyyJcdKkSXK73eeUp6amSpKGDx9eojwmJkZTp061JBYAsMInn3yiv/zlL5KknTt3auvWrRo7dqy++eYbSUXfkcaPH6+PP/5YL774oiRp9+7dfku8SjvPnu8cK3GexaWrlklXg6ioSxrevUztAqgSatSoIafTqYKCAoWEhCgoKKjM69asWbPU1xXF6ayWp24AASgsLOyc16WVVeR5l3MsrFAtf6sq2wTGycnJdocAVDtNmjTRggUL9MUXX+i+++5T7dq1y7zutGnTFBUVpVq1amnixImWxcjVVACB7s4779Ts2bP1448/asKECZKkOXPm6IorrlBMTIxGjhwpSbrrrrs0a9Ysbd++Xb/73e/8tn3Os6gohmmadsdQLrGxsabL5SpRVlhYqF27dqlt27blulpdlVX1fT58+LAkqWHDhjZHAn/geAYOjmXg4FgGDo5l4OBYBhbDMFJN04wtS10G0gAAAAAAC5F0AQAAAICFSLoAAAAAwEIkXQAAAABgIZIuAAAAALBQtRwy/rHRD+tY+hG/txsR1UAz5sz1e7sAAAAAqq5qmXQdSz+ilzvu93u7z5ZjcvQWLVooNDRUoaGhRes++6x+/etf+z0mAAAAAPaqlklXZTF//ny1b9/e7jAAAAAAWIhnugAAAADAQiRdNnrwwQd17bXXatSoUTpyxP/PmAEAAACwH0mXTb777jtt3rxZGzZsUFRUlEaMGGF3SAAAAAAswDNdNmnWrJkkqUaNGpowYYLatm1rc0QAAAAArEBPlw2ys7OVmZnpe//++++rc+fONkYEAAAAwCrVsqcrIqpBuYZ3L0+7ZZGWlqahQ4eqsLBQpmmqVatWSkhI8H9AAAAAAGxXLZMuuycwbtWqlTZu3GhrDAAAAAAqBrcXAgAAAICFSLoAAAAAwEIkXQAAAABgIZIuAAAAALAQSRcAAAAAWKhajl748CMP62j6Ub+3Wz+qvubOtndkRAAAAACVS7VMuo6mH1VG7wz/N/y/slfNy8vTU089pUWLFik4OFidO3fWO++84/+YAAAAANiqWiZdlcGkSZPkcDi0a9cuGYahgwcP2h0SAAAAAAuQdNkgOztbc+fOlcvlkmEYkqTGjRvbHBUAAAAAKzCQhg327t2rqKgoTZkyRV27dlWvXr20ZMkSu8MCAAAAYAHLky7DMKYbhpFsGIZpGEb7M8rbGIax0jCMXYZhrDUM4xqrY6ksCgoKtG/fPl1zzTVav369ZsyYofvuu09HjhyxOzQAAAAAflYRPV3zJfWUtP+s8tmS5pim2VbSK5L+XQGxVArNmzeXw+HQgw8+KEnq2LGjWrZsqR9//NHmyAAAAAD4m+VJl2ma35mm6TqzzDCMhpK6SCoeru8TSS0Nw2hhdTyVQf369dWvXz8tWrRIkrR//34lJSXpyiuvtDkyAAAAAP5m10AaTSW5TdP0SJJpmqZhGCmSmklKPrOiYRhPSnqy+H14ePhlb7x+VP1yDe9ernbLaNasWYqLi9MzzzyjoKAgzZkzp1oNptGwYUO7Q4AfcTwDB8cycHAsAwfHMnBwLKsvO0cvNM96b5RayTRfk/Ra8fvY2Niz1yu3yjCBcatWrZSYmGh3GAAAAAAsZtfohQckxRqG4ZQko2jc9KaSUmyKBwAAAAAsYUvSZZrmYUkbJQ07XTRUUrJpmsl2xAMAAAAAVqmIIePfNAzDJSlW0mLDMPacXvSIpEcMw9glaZKkkVbHAgAAAAAVzfJnukzTfFTSo6WU75TUw+rtAwAAAICd7HqmCwAAAACqBZIuAAAAALCQnUPG22ZcXJwyjhzxe7uRDRpoZny839sFAAAAUHVVy6Qr48gRjT2e5fd2/68cdRctWqQ//OEP8nq9Kigo0NNPP60RI0b4PSYAAAAA9qqWSZfdTNPUAw88oGXLlqlDhw5KTk7WVVddpSFDhqhOnTp2hwcAAADAj3imy0aZmZmSpKysLEVFRSkkJMTmiAAAAAD4Gz1dNjAMQx999JGGDBmiWrVq6dixY/rPf/6j4OBgu0MDAAAA4Gf0dNnA4/Ho5Zdf1meffab9+/dryZIlGjFihDIyMuwODQAAAICfkXTZYNOmTXK73frFL34hSerWrZtiYmK0efNmmyMDAAAA4G8kXTZo2rSpXC6Xdu7cKUnas2eP9u7dq7Zt29ocGQAAAAB/q5bPdEU2aFCu4d3L025ZREdHa/bs2brnnnvkcDhkmqZmzpypJk2aWBAVAAAAADsZpmnaHUO5xMbGmi6Xq0RZYWGhdu3apbZt2yooKMimyCpWddxnAAAAoLIwDCPVNM3YstTl9kIAAAAAsBBJFwAAAABYKCCSLsMwJElV7VbJy1G8r8X7DgAAAKByCoiBNBwOh2rUqKH09HRFRUUFfCJimqbS09NVo0YNORwBkTcDAAAAASsgki5JatasmVJSUqrNBMM1atRQs2bN7A4DAAAAwEUETNIVHBys1q1by+v1BvxthoZh0MMFAAAAVBEBk3QVIxkBAAAAUJmQoQAAAACAhUi6AAAAAMBCJF0AAAAAYCGjqg06YRhGnqQjdsdRAWpLOml3EPALjmXg4FgGDo5l4OBYBhaOZ+CoDseygWmaIWWpWOWSrurCMAyXaZqxdseBy8exDBwcy8DBsQwcHMvAwvEMHBzLkri9EAAAAAAsRNIFAAAAABYi6aq8XrM7APgNxzJwcCwDB8cycHAsAwvHM3BwLM/AM10AAAAAYCF6ugAAAADAQiQOhDOkAAAMEklEQVRdAAAAAGAhkq5qwjCMPoZh3GZ3HIHEMIyuhmG8e55lLQzDOHqR9e82DONVa6IDAothGKGGYXxqGMb/b+/Og7Wq6ziOvz9XUUnMUFErF9wVQXGmScUWMktUxi1bXFBKK1v8QwJHrYwxx6ykZsgYI1yS0dIkixQVTcUFXBEuSMEYWVmRmUujpbh8++P3feL0eBfu5T48914+r5ln+J3fWZ7fOb/n/M5vO5cVkhZJuk3S0AZ8j8vKXqCj8rWNbRdJGtjoNFn3rU0eSbpH0tj1lSZbY23qLH2FpGMlvbfZ6WiLG10bjtGAKxI9KCIejYiT12H/2RExqSfTZNbPTQf2ioiRwM253NNG082yUtLGPZuUDVdXyteIGBkR/2l0mqz7nEe2Hh0LuNHVH0k6WNJ9khZLapV0TPbQLcjlhyUdktsOlfSspIskPS7pd7nt9Mq278ptx0u6Q9Ks7CGaJ2mnXDciv3OhpGWSzqukZ0tJMyQtyTRdKWkkcCZwah7rgmZcq75CUkg6L/NjpaTDJH0r8+wJSfvmdqMlPVrZ70uSnpR0H3BGJX6IpLmZJ62Srsr48ZJuzPAFmTeLcruQtHOum5hpWShpjqQd1+sF2UBIGijp+rynFmee1efxcElPZXiGpK9U1u0qaZWkAU1Ifr8XEa9ExJxY89efHgR2ldQi6bIsTxdLekzSZgCSDpd0f8Y9JOkDGb+9pLsz/glJU1W0WVZ2cJzRud1USQuA49b7heljulO+Vp6dF2YePCnpyLpjDpJ0iqRfV+Il6Q+S9msvz3O7yZKuk/TrvP/vkrTV+r42fV3mw2RJD6iMSJ9Yt25QhveRdHs+D1slndnGsU7Ie2u3Tsrh2m/j0rw3n5B06Ho43V6tq8+zSlyb11HSUZIeyWMtknRgxo9RqZu0qtRTh2V8rWy8XKVOszC/r5amOyq/h8mSblCp3yyVNFvS4Fz3YZX69OO57tOVNL1b0o2V39E3s1w4Gjg3v/8MepOI8KebH2ArYBUwKpdbgG2BPwGHZ9z7gL8BmwNDgQCOynWTgBeAkbk8Dbg4w+OB/1B6dQHOAeZkeAtg0wwPBB4H3pPLVwE/AFpyeUj+Oxm4tNnXrC98Mo++lOGPAy9X8uwc4LoMjwYezfB+wF+B7Sp5+WyGzwamV383lTy+sY3vnw5ckeGTcnmjXB4H/KrZ16g/figV5rnVfKrmccYNB57K8ChgSWXdhcCUZp/HhvIBrgG+DxwA/LZS5m2ZZfGuwHzg7Rm/O/AXYACwGTAo4zeijJqdkMv/V1Z2cpzRwJvA+5p9PfrKp5vl69Dc75hcHgMsrzvmIMrz8Flg+4w/FHgsw53l+e8rZfPPgPOafa362ifz4RsZ3jXzYse6PNoYWAF8orLfNvnvPcBY4CvAvEp+dFQO134bp+XyQZR62ebNvh5NzouuPs/avY7AnhneM9cNoJSz22Yej8j4k4GllTx7jTX12x8CTwM75PIc4HMZnkypJ1frT9MyPJg19Z+tgKeAd+by3cCkyvnU6rtXA19udh609fFI17o5GFgWEfMBIuJNYDtgdUTcnnH3A89QKuUAL0XELRleCDwdEYty+TFKQVVzf0Qsz/B04EPZMzcQmCFpCaW3d2dgZG43FvhupoWI+EdPnvAG5Pr8dyHwZiXP6vOoZjRwS0T8PZer054eBMZImiLpaEolo02SvgbsBHw+o44FDgMek7SIUinZueunY2thMbC3pGmSPkl5YLQr7/sBKqPVAk6ldHpYg0k6H9gD+CqwklIJuFLSacCALP/GUBpI9+a9c2PuviOlUfZtSYvJTivWlKH1OjoOwIos523tdbV8BXg5In6V4QXAbvUbRJm+Ngs4JaPGs+ae7CzPb42I5zo6vq2VGQARsRK4H3h/3fq9gI0j4oZaRERU3yWaDHwQ+GglPzqzGpiZx3qQ0kDYvzuJ70e69DxL7V3Hj1A6/Vfkutci4kXgQGBRRCzJ+GuBHSS9M4+3vFK/XZjbPp3L9ff6zXX1p8MyvDXwc0lLgbuAbYB9c5RsFKXjjfz+Xl/fdaOr54nSW1CvFvdqJe4N4JW65bV5J+Bi4O/AARGxP6V3aLMup9Q6UsuXN3hrnrWVR2rvQBGxgPJwfwj4GPCIpI3ecgBpHKV36oSIeL1y3IuizIcfGREjorzPYj0sKwnDgNuAQ4CllPyu5lX9fXY1pWJ3KPBMRCxteEI3cJImAscDR0TEv/Phvy9wHbA30Cppd8q9c1vl3hkZEe/OfJ5AeZgfGBH75b7tlaEdHQfgpcadbb/V1fK1uk9tu7eUoekqYLykt1M6Ia/L+M7yvDvPYutcV/8z2AWU+3iXStzrdFwO98T39ivdfJ61eagO1nVW362/p7pyj9WOcTll1LNW91lBH67vutG1buYD+0gaBSCphdIzsGltLmyu2xZY0o3jHyJpzwyfAdwVZex0MGWE7HVJe1F6IWpmA5MyLUgakvH/ogwHW2PcDRwpadtcPr22QtIulBHOG4CzKEP1g6o7S/owcAEwNiKqlbjZwBdr7xdIGiDpgMadxoZL0g5ARMRsYCLlgdIC7CJp69xsXN1uP6FMkToTj3I1nKQJwInARyLihYwbQplKNBc4nzL9ZBgwlzLCPLyyf+3l6sHAqoh4RdJ2lDysqS8rOzqO9TLZQ98CfAe4ozJa0lGeW8/5DJR3rSivV9SPAi8HVkv63/WXtE1l/e2U+s7NKu9YAvyBjsvhTShT22r35vZA67qeSF/WzedZe9fxduCIWn006yFbUhrIIyXtk/GfotRNV3UjyUfV1Z/uzPBg4I8RESrv0u5PObGXKL+tsyvn3Ovru+7JWQcR8byk44ApkragtMy/ThnNmCppc0rL/uMR8XLlB7G25gGT88XEFynTlwAuAmZKOplSwbirss/ZlOHWpZJWA48AnwVuAsbl9JhfRMSFXT9ja09EtEq6GJgvaRVwS2X1aGCCpFov06SIeLHMSPufrwFvA26txB8ZETOzgLxHUlDu2Sso02OsZ40ALsmpgi3AzIiYJ+lS4FGVF47vre4QEX9TeTF5LOU+swbJSsQUynTCu/M+eRX4AvBjlT9g0kLpDLs1Il6TdAplKvZASoViIaVSMZUyZWUR5f2sOytf9ZaysoPjWO90FaXRdUQlrqM8t57zqqQHgCHAWRHx5+rK7Cw+BrhM5Q/VBOV9nx9VtrlX5Y9wzJJ0SkQs6KgcBv4J7C7pIUqH5kkR0e40/g1El59ntH8dn5R0OvDTLGffAD4fEQ/nDJ1rc/bOC8Anupne3wBXZCf1SuC0jD8XmCbpXGAZZcZQzTjgB5KeoIyG/hL4BmWK5NXZsL8sImZ0M009TmXgxHobSeMpox4nNDstZmZmZh3JjsEt6mZrNPo7h1L+OMQ2nWxqvZSkyZQ/cjOx2WlpNE8vNDMzMzMzayCPdJmZmZmZmTWQR7rMzMzMzMwayI0uMzMzMzOzBnKjy8zMzMzMrIHc6DIzMzMzM2sgN7rMzKxfkvRU9T81NjMzaxY3uszMzMzMzBrIjS4zM+vzJB0s6T5JiyW1Sjqmbv0ESY9IelzSw5IOzPiBkq6XtCz3nZvxe0h6IOOWSLqoGedlZmb9w8bNToCZmdm6kLQVcBNwfETMl9QCvKNus5kR8b3c/iDgCmA4MAYYHBHDKscC+DJwS0RcXBdvZmbWZW50mZlZX3cwsCwi5gNExJvAc5Kq2xwg6avA1sDrwDBJmwCLgb0lTQPmAXNy+3uB70raPOPvXC9nYmZm/ZKnF5qZWb+WjatZwISIGA58ABCwSUSsBIYBtwGHAEslDY6IWbm8nDLqdXNTEm9mZv2CG11mZtbXzQf2kTQKQFJL3XTAzYABwJ9z+azaCkk7ABERs4GJlMbYjpL2AJ6JiGuAc4CDGn8aZmbWX3l6oZmZ9WkR8byk44ApkrYAAvh6Zf2/JF0APCzpT8Dsyu4jgEtU5iK2UN79apV0PnCypNWUhtiZ6+t8zMys/1FENDsNZmZmZmZm/ZanF5qZmZmZmTWQG11mZmZmZmYN5EaXmZmZmZlZA7nRZWZmZmZm1kBudJmZmZmZmTWQG11mZmZmZmYN5EaXmZmZmZlZA7nRZWZmZmZm1kD/BYBgYjK/GWaOAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1040x800 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### 包点 + 箱形图(Dot + Box Plot)\n",
    "# 包点 + 箱形图传达类似于分组的箱形图信息. 可以了解每组中有多少数据点.\n",
    "# import data\n",
    "df = pd.read_csv(r\"F:\\PythonProject\\AI\\Python\\DataAnalysis\\datasets\\mpg_ggplot2.csv\")\n",
    "\n",
    "# Draw Plot\n",
    "plt.figure(figsize=(13,10),dpi= 80)\n",
    "sns.boxplot(x='class',y='hwy',data=df,hue='cyl')\n",
    "sns.stripplot(x='class',y='hwy',data=df,color='black',size=3,jitter=1)\n",
    "\n",
    "for i in range(len(df['class'].unique())-1):\n",
    "    plt.vlines(i+.5,10,45, linestyles='solid',colors='gray',alpha=0.2)\n",
    "    \n",
    "# Decoration\n",
    "plt.title('Box Plot of Highway Mileage by Vehicle Class',fontsize=22)\n",
    "plt.legend(title='Cylinders')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1040x800 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### 小提琴图(Violin Plot)\n",
    "# 小提琴的形状或面积取决于所持有的观察次数\n",
    "# import data\n",
    "df = pd.read_csv(r\"F:\\PythonProject\\AI\\Python\\DataAnalysis\\datasets\\mpg_ggplot2.csv\")\n",
    "\n",
    "# Draw Plot\n",
    "plt.figure(figsize=(13,10),dpi= 80)\n",
    "sns.violinplot(x='class',y='hwy',data=df,scale='width',inner='quartile')\n",
    "\n",
    "# Decoration\n",
    "plt.title('Violin Plot of Highway Mileage by Vehicle Class',fontsize=22)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1040x800 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### 人口金字塔(Population Pyramid)\n",
    "# 人口金字塔用于显示由数量排序的组的分布. 它可用于显示人口的逐级过滤.\n",
    "# 用于显示由多少人通过营销渠道的每个阶段.\n",
    "# Read data\n",
    "# import data\n",
    "df = pd.read_csv(r\"F:/PythonProject/AI/Python/DataAnalysis/datasets/email_campaign_funnel.csv\")\n",
    "\n",
    "# Draw Plot\n",
    "plt.figure(figsize=(13,10),dpi=80)\n",
    "group_col = 'Gender'\n",
    "order_of_bars = df.Stage.unique()[::-1]\n",
    "colors = [plt.cm.Spectral(i/float(len(df[group_col].unique())-1)) for i in \n",
    "         range(len(df[group_col].unique()))]\n",
    "\n",
    "for c,group in zip(colors,df[group_col].unique()):\n",
    "    sns.barplot(x='Users',y='Stage',data=df.loc[df[group_col]==group,:],\n",
    "               order=order_of_bars,color=c,label=group)\n",
    "    \n",
    "# Decorations\n",
    "plt.xlabel(\"$Users$\")\n",
    "plt.ylabel(\"Stage of Purchase\")\n",
    "plt.yticks(fontsize=12)\n",
    "plt.title(\"Population Pyramid of the Marketing Funnel\",fontsize=22)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'fig' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-9-338575a8b592>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      9\u001b[0m                \u001b[0mkind\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"count\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mheight\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m3.5\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0maspect\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m.8\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     10\u001b[0m                palette='tab20')\n\u001b[1;32m---> 11\u001b[1;33m \u001b[0mfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msuptitle\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'sf'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     12\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'fig' is not defined"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 806.4x504 with 7 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### 分类图(Categorical Plots)\n",
    "# 由seaborn库提供的分类图可用于可视化彼此相关的2个或更多分类变量的计数分布.\n",
    "# Load Dataset\n",
    "titanic = sns.load_dataset(\"titanic\")\n",
    "\n",
    "# Plot\n",
    "g = sns.catplot(\"alive\",col=\"deck\",col_wrap=4,\n",
    "               data=titanic[titanic.deck.notnull()],\n",
    "               kind=\"count\",height=3.5,aspect=.8,\n",
    "               palette='tab20')\n",
    "fig.suptitle('sf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1c66a7b8e48>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1150.5x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Load Dataset\n",
    "titanic = sns.load_dataset(\"titanic\")\n",
    "\n",
    "# Plot\n",
    "sns.catplot(x=\"age\",y=\"embark_town\",\n",
    "           hue=\"sex\",col=\"class\",\n",
    "           data=titanic[titanic.embark_town.notnull()],\n",
    "           orient=\"h\",height=5,aspect=1,palette=\"tab10\",\n",
    "           kind=\"violin\",dodge=True,cut=0,bw=.2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\n输出内容如下:\\nUserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\\n  warnings.warn(\"This figure includes Axes that are not compatible \\n  \\n使用上下文管理器禁止来自特定函数调用的警告:\\n#import warnings\\n#with warnings.catch_warnings():\\n#    warnings.simplefilter(\"ignore\")\\n#    fig.tight_layout()\\n    \\n#忽略matplotlib的所有警告:\\n#warnings.filterwarnings(\"ignore\", module=\"matplotlib\")\\n\\n#要忽略matplotlib中的UserWarnings:\\nwarnings.filterwarnings(\"ignore\", category=UserWarning, module=\"matplotlib\")\\n'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Waffle size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## 组成(Composition)\n",
    "### 华夫饼图(Waffle Chart)\n",
    "# 可以用pywaffle包创建华夫饼图,用于显示更大群体中的组的组成.\n",
    "# pip install pywaffle\n",
    "from pywaffle import Waffle\n",
    "import warnings\n",
    "\n",
    "warnings.filterwarnings(\"ignore\", category=UserWarning, module=\"matplotlib\")\n",
    "\n",
    "# Import data\n",
    "df_raw = pd.read_csv(r\"F:/PythonProject/AI/Python/DataAnalysis/datasets/mpg_ggplot2.csv\")\n",
    "\n",
    "# Prepare Data\n",
    "df = df_raw.groupby('class').size().reset_index(name='counts')\n",
    "n_categories = df.shape[0]\n",
    "colors = [plt.cm.inferno_r(i/float(n_categories)) for i in range(n_categories)]\n",
    "\n",
    "# Draw Plot and Decorate\n",
    "fig = plt.figure(\n",
    "    FigureClass=Waffle,\n",
    "    plots={\n",
    "        '111': {\n",
    "            'values': df['counts'],\n",
    "            'labels':[\"{0} ({1})\".format(n[0],n[1]) \n",
    "                      for n in df[['class','counts']].itertuples()],\n",
    "            'legend': {'loc': 'upper left','bbox_to_anchor': (1.05,1), 'fontsize':12},\n",
    "            'title': {'label': '# Vehicles by Class','loc': 'center','fontsize':18}\n",
    "        },\n",
    "    },\n",
    "    rows=7,\n",
    "    colors=colors,\n",
    "    figsize=(16,9))\n",
    "\n",
    "\"\"\"\n",
    "#输出内容如下:\n",
    "#UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
    "#  warnings.warn(\"This figure includes Axes that are not compatible \n",
    "  \n",
    "使用上下文管理器禁止来自特定函数调用的警告:\n",
    "#import warnings\n",
    "#with warnings.catch_warnings():\n",
    "#    warnings.simplefilter(\"ignore\")\n",
    "#    fig.tight_layout()\n",
    "    \n",
    "#忽略matplotlib的所有警告:\n",
    "#warnings.filterwarnings(\"ignore\", module=\"matplotlib\")\n",
    "\n",
    "#要忽略matplotlib中的UserWarnings:\n",
    "warnings.filterwarnings(\"ignore\", category=UserWarning, module=\"matplotlib\")\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "f:\\pythonproject\\env\\scriptenv\\lib\\importlib\\_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n",
      "  return f(*args, **kwds)\n",
      "f:\\pythonproject\\env\\scriptenv\\lib\\importlib\\_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n",
      "  return f(*args, **kwds)\n",
      "f:\\pythonproject\\env\\scriptenv\\lib\\importlib\\_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n",
      "  return f(*args, **kwds)\n",
      "f:\\pythonproject\\env\\scriptenv\\lib\\site-packages\\matplotlib\\figure.py:2369: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
      "  warnings.warn(\"This figure includes Axes that are not compatible \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Waffle size 1152x1008 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np \n",
    "# import matplotlib as mpl \n",
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns \n",
    "import matplotlib\n",
    "from pywaffle import Waffle\n",
    "import pandas as pd  \n",
    "\n",
    "df_raw = pd.read_csv(r\"F:/PythonProject/AI/Python/DataAnalysis/datasets/mpg_ggplot2.csv\")\n",
    "# Prepare Data\n",
    "# By Class Data\n",
    "df_class = df_raw.groupby('class').size().reset_index(name='counts_class')\n",
    "n_categories = df_class.shape[0]\n",
    "colors_class = [plt.cm.Set3(i/float(n_categories)) for i in range(n_categories)]\n",
    "\n",
    "# By Cylinders Data\n",
    "df_cyl = df_raw.groupby('cyl').size().reset_index(name='counts_cyl')\n",
    "n_categories = df_cyl.shape[0]\n",
    "colors_cyl = [plt.cm.Spectral(i/float(n_categories)) for i in range(n_categories)]\n",
    "\n",
    "# By Make Data\n",
    "df_make = df_raw.groupby('manufacturer').size().reset_index(name='counts_make')\n",
    "n_categories = df_make.shape[0]\n",
    "colors_make = [plt.cm.tab20b(i/float(n_categories)) for i in range(n_categories)]\n",
    "\n",
    "# Draw Plot and Decorate\n",
    "fig = plt.figure(\n",
    "        FigureClass=Waffle,\n",
    "        plots={\n",
    "            '311': {\n",
    "                'values': df_class['counts_class'],\n",
    "                'labels': [\"{1}\".format(n[0],n[1]) \n",
    "                           for n in df_class[['class','counts_class']].itertuples()],\n",
    "                'legend': {'loc': 'upper left','bbox_to_anchor': (1.05,1),\n",
    "                          'fontsize': 12, 'title':'Class'},\n",
    "                'title': {'label': '# Vehicles by Class','loc': 'center','fontsize':18},\n",
    "                'colors': colors_class\n",
    "            },\n",
    "            '312': {\n",
    "                'values': df_cyl['counts_cyl'],\n",
    "                'labels': [\"{1}\".format(n[0],n[1]) for n in df_cyl[['cyl','counts_cyl']].itertuples()],\n",
    "                'legend': {'loc':'upper left','bbox_to_anchor': (1.05,1),'fontsize':12,'title':'Cyl'},\n",
    "                'title': {'label': '# Vehicles by Cyl','loc': 'center','fontsize': 18},\n",
    "                'colors': colors_cyl\n",
    "            },\n",
    "            '313': {\n",
    "                'values': df_make['counts_make'],\n",
    "                'labels': [\"{1}\".format(n[0],n[1]) for n in df_make[['manufacturer',\n",
    "                                                                    'counts_make']].itertuples()],\n",
    "                'legend': {'loc': 'upper left','bbox_to_anchor': (1.05,1),'fontsize':12,'title': 'Manufacturer'},\n",
    "                'title': {'label': '# Vehicles by Make','loc':'center','fontsize':18},\n",
    "                'colors': colors_make\n",
    "            }\n",
    "        },\n",
    "        rows=9,\n",
    "        figsize=(16,14)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### 饼图\n",
    "# 饼图是显示组成的经典方式. 使用饼图时,建议明确记下饼图每个部分的百分比或数字.\n",
    "\n",
    "# Import \n",
    "df_raw = pd.read_csv(r\"F:/PythonProject/AI/Python/DataAnalysis/datasets/mpg_ggplot2.csv\")\n",
    "\n",
    "# Prepare Data\n",
    "df = df_raw.groupby('class').size()\n",
    "\n",
    "# Make the plot with pandas\n",
    "df.plot(kind='pie',subplots=True,figsize=(8,8))\n",
    "plt.title(\"Pie Chart of Vehicle Class - Bad\")\n",
    "plt.ylabel(\"\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "Unknown property colors",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-2-b40fbb73c1ed>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     32\u001b[0m                                 \u001b[0mcolors\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcm\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDark2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolors\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     33\u001b[0m                                 \u001b[0mstartangle\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m140\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 34\u001b[1;33m                                 explode=explode)\n\u001b[0m\u001b[0;32m     35\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     36\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mf:\\pythonproject\\env\\scriptenv\\lib\\site-packages\\matplotlib\\__init__.py\u001b[0m in \u001b[0;36minner\u001b[1;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1808\u001b[0m                         \u001b[1;34m\"the Matplotlib list!)\"\u001b[0m \u001b[1;33m%\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mlabel_namer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1809\u001b[0m                         RuntimeWarning, stacklevel=2)\n\u001b[1;32m-> 1810\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1811\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1812\u001b[0m         inner.__doc__ = _add_data_doc(inner.__doc__,\n",
      "\u001b[1;32mf:\\pythonproject\\env\\scriptenv\\lib\\site-packages\\matplotlib\\axes\\_axes.py\u001b[0m in \u001b[0;36mpie\u001b[1;34m(self, x, explode, labels, colors, autopct, pctdistance, shadow, labeldistance, startangle, radius, counterclock, wedgeprops, textprops, center, frame, rotatelabels)\u001b[0m\n\u001b[0;32m   2880\u001b[0m             \u001b[0mprops\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtextprops\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2881\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2882\u001b[1;33m             \u001b[0mt\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtext\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mxt\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0myt\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mprops\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2883\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2884\u001b[0m             \u001b[0mtexts\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mt\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mf:\\pythonproject\\env\\scriptenv\\lib\\site-packages\\matplotlib\\axes\\_axes.py\u001b[0m in \u001b[0;36mtext\u001b[1;34m(self, x, y, s, fontdict, withdash, **kwargs)\u001b[0m\n\u001b[0;32m    725\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mfontdict\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    726\u001b[0m             \u001b[0mt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfontdict\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 727\u001b[1;33m         \u001b[0mt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    728\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    729\u001b[0m         \u001b[0mt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_clip_path\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mf:\\pythonproject\\env\\scriptenv\\lib\\site-packages\\matplotlib\\text.py\u001b[0m in \u001b[0;36mupdate\u001b[1;34m(self, kwargs)\u001b[0m\n\u001b[0;32m    185\u001b[0m         \u001b[0msentinel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mobject\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m  \u001b[1;31m# bbox can be None, so use another sentinel.\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    186\u001b[0m         \u001b[0mbbox\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"bbox\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msentinel\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 187\u001b[1;33m         \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    188\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mbbox\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0msentinel\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    189\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_bbox\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbbox\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mf:\\pythonproject\\env\\scriptenv\\lib\\site-packages\\matplotlib\\artist.py\u001b[0m in \u001b[0;36mupdate\u001b[1;34m(self, props)\u001b[0m\n\u001b[0;32m    914\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    915\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mcbook\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_setattr_cm\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0meventson\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 916\u001b[1;33m             \u001b[0mret\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0m_update_property\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mv\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mv\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mprops\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    917\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    918\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mret\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mf:\\pythonproject\\env\\scriptenv\\lib\\site-packages\\matplotlib\\artist.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m    914\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    915\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mcbook\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_setattr_cm\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0meventson\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 916\u001b[1;33m             \u001b[0mret\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0m_update_property\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mv\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mv\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mprops\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    917\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    918\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mret\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mf:\\pythonproject\\env\\scriptenv\\lib\\site-packages\\matplotlib\\artist.py\u001b[0m in \u001b[0;36m_update_property\u001b[1;34m(self, k, v)\u001b[0m\n\u001b[0;32m    910\u001b[0m                 \u001b[0mfunc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'set_'\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    911\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mcallable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 912\u001b[1;33m                     \u001b[1;32mraise\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Unknown property %s'\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    913\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mv\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    914\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: Unknown property colors"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x560 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "import numpy as np \n",
    "import matplotlib as mpl \n",
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns \n",
    "import matplotlib\n",
    "from pywaffle import Waffle\n",
    "import pandas as pd  \n",
    "\n",
    "# pd.set_option('display.mpl_style','default')\n",
    "matplotlib.style.use('ggplot')\n",
    "# Import\n",
    "df_raw = pd.read_csv(r\"F:/PythonProject/AI/Python/DataAnalysis/datasets/mpg_ggplot2.csv\")\n",
    "\n",
    "# Prepare Data\n",
    "df = df_raw.groupby('class').size().reset_index(name='counts')\n",
    "\n",
    "# Draw Plot\n",
    "fig,ax = plt.subplots(figsize=(12,7),subplot_kw=dict(aspect=\"equal\"),dpi=80)\n",
    "\n",
    "data = df['counts']\n",
    "categories = df['class']\n",
    "explode = [0,0,0,1,1,0.1,1]\n",
    "\n",
    "def func(pct,allvals):\n",
    "    absolute = int(pct/100.*np.sum(allvals))\n",
    "    return \"{:.1f}% ({:d})\".format(pct,absolute)\n",
    "\n",
    "wedges,texts,autotexts = ax.pie(data,\n",
    "                               autopct=lambda pct: func(pct,data),\n",
    "                                textprops=dict(colors=\"w\"),\n",
    "                                colors = plt.cm.Dark2.colors,\n",
    "                                startangle = 140,\n",
    "                                explode=explode)\n",
    "\n",
    "\n",
    "# Decoration\n",
    "ax.legend(wedges,categories,title=\"Vehicle Class\",loc=\"center left\",\n",
    "         bbox_to_anchor=(1,0,0.5,1))\n",
    "plt.setp(autotexts,size=10,weight = 700)\n",
    "ax.set_title(\"Class of Vehicles: Pie Chart\")\n",
    "plt.show()\n",
    "\n",
    "\"\"\"\n",
    "报错如下:\n",
    "    AttributeError: Unknown property %colors\n",
    "解决办法: \n",
    "pd.set_option('display.mpl_style','default')\n",
    "matplotlib.style.use('ggplot')\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x640 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "### 树形图(Treemap)\n",
    "# pip install squarify\n",
    "import squarify\n",
    "\n",
    "# Import Data\n",
    "df_raw = pd.read_csv(r\"F:/PythonProject/AI/Python/DataAnalysis/datasets/mpg_ggplot2.csv\")\n",
    "\n",
    "# Prepare Data\n",
    "df = df_raw.groupby('class').size().reset_index(name='counts')\n",
    "labels = df.apply(lambda x: str(x[0]) + \"\\n (\" + str(x[1]) + \")\", axis=1)\n",
    "sizes = df['counts'].values.tolist()\n",
    "colors = [plt.cm.Spectral(i/float(len(labels))) for i in range(len(labels))]\n",
    "\n",
    "# Draw Plot \n",
    "plt.figure(figsize=(12,8),dpi = 80)\n",
    "squarify.plot(sizes=sizes,label=labels,color=colors,alpha=.8)\n",
    "\n",
    "# Decorate\n",
    "plt.title('Treemap of Vechile Class')\n",
    "plt.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1280x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "### 条形图(Bar Chart)\n",
    "# 条形图是基于计数或任何给定指标可视化项目的经典方式.\n",
    "# 可通过在plt.plot()中设置颜色参数来更改条的颜色.\n",
    "import random\n",
    "\n",
    "# Import Data\n",
    "df_raw = pd.read_csv(r\"F:/PythonProject/AI/Python/DataAnalysis/datasets/mpg_ggplot2.csv\")\n",
    "\n",
    "# Prepare Data\n",
    "df = df_raw.groupby('manufacturer').size().reset_index(name='counts')\n",
    "n = df['manufacturer'].unique().__len__()+1\n",
    "all_colors = list(plt.cm.colors.cnames.keys())\n",
    "random.seed(100)\n",
    "c = random.choices(all_colors,k=n)\n",
    "\n",
    "# Plot Bars\n",
    "plt.figure(figsize=(16,10),dpi = 80)\n",
    "plt.bar(df['manufacturer'],df['counts'],color=c,width=.5)\n",
    "for i,val in enumerate(df['counts'].values):\n",
    "    plt.text(i,val,float(val),horizontalalignment='center',\n",
    "            verticalalignment='bottom',fontdict={'fontweight': 500,'size': 12})\n",
    "    \n",
    "# Decoration\n",
    "plt.gca().set_xticklabels(df['manufacturer'],rotation=60,horizontalalignment='right')\n",
    "plt.title(\"Number of Vehicles by Manaufacturers\",fontsize=22)\n",
    "plt.ylabel('# Vehicles')\n",
    "plt.ylim(0,45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Unrecognized character t in format string",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-8-3cacb71a0476>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[1;31m# Draw Plot\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      9\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m16\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mdpi\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m80\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 10\u001b[1;33m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'date'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'traffic'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mcolor\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'tab:red'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     11\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     12\u001b[0m \u001b[1;31m# Decoration\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mf:\\pythonproject\\env\\scriptenv\\lib\\site-packages\\matplotlib\\pyplot.py\u001b[0m in \u001b[0;36mplot\u001b[1;34m(scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[0;32m   2809\u001b[0m     return gca().plot(\n\u001b[0;32m   2810\u001b[0m         *args, scalex=scalex, scaley=scaley, **({\"data\": data} if data\n\u001b[1;32m-> 2811\u001b[1;33m         is not None else {}), **kwargs)\n\u001b[0m\u001b[0;32m   2812\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2813\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mf:\\pythonproject\\env\\scriptenv\\lib\\site-packages\\matplotlib\\__init__.py\u001b[0m in \u001b[0;36minner\u001b[1;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1808\u001b[0m                         \u001b[1;34m\"the Matplotlib list!)\"\u001b[0m \u001b[1;33m%\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mlabel_namer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1809\u001b[0m                         RuntimeWarning, stacklevel=2)\n\u001b[1;32m-> 1810\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1811\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1812\u001b[0m         inner.__doc__ = _add_data_doc(inner.__doc__,\n",
      "\u001b[1;32mf:\\pythonproject\\env\\scriptenv\\lib\\site-packages\\matplotlib\\axes\\_axes.py\u001b[0m in \u001b[0;36mplot\u001b[1;34m(self, scalex, scaley, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1609\u001b[0m         \u001b[0mkwargs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcbook\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnormalize_kwargs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmlines\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mLine2D\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_alias_map\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1610\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1611\u001b[1;33m         \u001b[1;32mfor\u001b[0m \u001b[0mline\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_lines\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1612\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd_line\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mline\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1613\u001b[0m             \u001b[0mlines\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mline\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mf:\\pythonproject\\env\\scriptenv\\lib\\site-packages\\matplotlib\\axes\\_base.py\u001b[0m in \u001b[0;36m_grab_next_args\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    391\u001b[0m                 \u001b[0mthis\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    392\u001b[0m                 \u001b[0margs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 393\u001b[1;33m             \u001b[1;32myield\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_plot_args\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mthis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    394\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    395\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mf:\\pythonproject\\env\\scriptenv\\lib\\site-packages\\matplotlib\\axes\\_base.py\u001b[0m in \u001b[0;36m_plot_args\u001b[1;34m(self, tup, kwargs)\u001b[0m\n\u001b[0;32m    340\u001b[0m         \u001b[0mret\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    341\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtup\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtup\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 342\u001b[1;33m             \u001b[0mlinestyle\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmarker\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolor\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_process_plot_format\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtup\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    343\u001b[0m             \u001b[0mtup\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtup\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    344\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtup\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m3\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mf:\\pythonproject\\env\\scriptenv\\lib\\site-packages\\matplotlib\\axes\\_base.py\u001b[0m in \u001b[0;36m_process_plot_format\u001b[1;34m(fmt)\u001b[0m\n\u001b[0;32m    116\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    117\u001b[0m             raise ValueError(\n\u001b[1;32m--> 118\u001b[1;33m                 'Unrecognized character %c in format string' % c)\n\u001b[0m\u001b[0;32m    119\u001b[0m         \u001b[0mi\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    120\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: Unrecognized character t in format string"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1280x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "## 变化(Change)\n",
    "### 时间序列图(Time Series Plot)\n",
    "# 时间序列图用于显示给定度量随时间变化的方式. 查看1949年至1969年间航空客运量的变化情况.\n",
    "# Import Data\n",
    "df = pd.read_csv(r\"F:/PythonProject/AI/Python/DataAnalysis/datasets/AirPassengers.csv\")\n",
    "\n",
    "# Draw Plot\n",
    "plt.figure(figsize=(16,10),dpi = 80)\n",
    "plt.plot('date','traffic',data=df,color='tab:red')\n",
    "\n",
    "# Decoration\n",
    "plt.ylim(50,750)\n",
    "xtick_location = df.index.tolist()[::12]\n",
    "xtick_labels = [x[-4:] for x in df.date.tolist()[::12]]\n",
    "plt.xticks(ticks=xtick_location,labels = xtick_labels,rotation=0,fontsize=12,\n",
    "          horizontalalignment='center',alpha=.7)\n",
    "plt.yticks(fontsize=12,alpha=.7)\n",
    "plt.title(\"Air Passengers Traffic (1949 - 1969)\",fontsize=22)\n",
    "plt.grid(axis='both',alpha=.3)\n",
    "\n",
    "# Remove borders\n",
    "plt.gca().spines[\"top\"].set_alpha(0.0)\n",
    "plt.gca().spines[\"bottom\"].set_alpha(0.3)\n",
    "plt.gca().spines[\"right\"].set_alpha(0.0)\n",
    "plt.gca().spines[\"left\"].set_alpha(0.3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'traffic'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32mf:\\pythonproject\\env\\scriptenv\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   2656\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2657\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2658\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'traffic'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-10-816aa69d1786>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[1;31m# Get the Peaks and Troughs\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 8\u001b[1;33m \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'traffic'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      9\u001b[0m \u001b[0mdoublediff\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdiff\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msign\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdiff\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     10\u001b[0m \u001b[0mpeak_locations\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwhere\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdoublediff\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;33m-\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mf:\\pythonproject\\env\\scriptenv\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   2925\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2926\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2927\u001b[1;33m             \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2928\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2929\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mf:\\pythonproject\\env\\scriptenv\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   2657\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2658\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2659\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2660\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2661\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msize\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'traffic'"
     ]
    }
   ],
   "source": [
    "\n",
    "### 带波峰波谷标记的时序图(Time Series with Peaks and Troughs Annotated)\n",
    "# 时间序列绘制了所有峰值和低谷,并注释了所选特殊时间的发生.\n",
    "# Import Data\n",
    "df = pd.read_csv(r\"F:/PythonProject/AI/Python/DataAnalysis/datasets/AirPassengers.csv\")\n",
    "\n",
    "# Get the Peaks and Troughs\n",
    "data = df['traffic'].values\n",
    "doublediff = np.diff(np.sign(np.diff(data)))\n",
    "peak_locations = np.where(doublediff == -2)[0] + 1\n",
    "\n",
    "doublediff2 = np.diff(np.sign(np.diff(-1*data)))\n",
    "trough_locations = np.where(doublediff2 == -2)[0] + 1 \n",
    "\n",
    "# Draw Plot\n",
    "plt.figure(figsize=(16,10),dpi = 80)\n",
    "plt.plot('date','traffic',data=df,color='tab:blue',label='Air Traffic')\n",
    "plt.scatter(df.date[peak_locations],df.traffic[peak_locations],\n",
    "           marker=mpl.markers.CARETUPBASE,color='tab:green',s=100,label='Peaks')\n",
    "plt.scatter(df.date[trough_locations],df.traffic[trough_locations],\n",
    "           markers=mpl.markers.CARETUPBASE,color='tab:red',s=100,label='Troughs')\n",
    "\n",
    "# Annotate\n",
    "for t,p in zip(trough_locations[1::5],peak_locations[::3]):\n",
    "    plt.text(df.date[p],df.traffic[p] + 15, df.date[p],horizontalalignment='center',color='darkgreen')\n",
    "    plt.text(df.date[t],df.traffic[t]-35,df.date[t],horizontalalignment='center',color='darkred')\n",
    "    \n",
    "    \n",
    "# Decoration\n",
    "plt.ylim(50,750)\n",
    "xtick_location = df.index.tolist()[::6]\n",
    "xtick_labels = df.date.tolist()[::6]\n",
    "plt.xticks(ticks=xtick_location,labels=xtick_labels,rotation=90,fontsize=12,alpha=.7)\n",
    "plt.title(\"Peak and Troughs of Air Passengers Traffic (1949 - 1969)\",fontsize=22)\n",
    "\n",
    "plt.yticks(fontsize=12,alpha=.7)\n",
    "\n",
    "# Lighten borders\n",
    "plt.gca().spines[\"top\"].set_alpha(.0)\n",
    "plt.gca().spines[\"bottom\"].set_alpha(.3)\n",
    "plt.gca().spines[\"right\"].set_alpha(.0)\n",
    "plt.gca().spines[\"left\"].set_alpha(.3)\n",
    "\n",
    "plt.legend(loc='upper left')\n",
    "plt.grid(axis='y',alpha=.3)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "f:\\pythonproject\\env\\scriptenv\\lib\\importlib\\_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n",
      "  return f(*args, **kwds)\n"
     ]
    },
    {
     "ename": "AttributeError",
     "evalue": "'DataFrame' object has no attribute 'traffic'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-2-3891f483c862>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     18\u001b[0m \u001b[1;31m# Draw Plot\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     19\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0max2\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msubplots\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m16\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m6\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mdpi\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m80\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 20\u001b[1;33m \u001b[0mplot_acf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtraffic\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtolist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0max1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mlags\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m50\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     21\u001b[0m \u001b[0mplot_pacf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtraffic\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtolist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0max2\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mlags\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m20\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     22\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mf:\\pythonproject\\env\\scriptenv\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m__getattr__\u001b[1;34m(self, name)\u001b[0m\n\u001b[0;32m   5065\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_can_hold_identifiers_and_holds_name\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5066\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 5067\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   5068\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5069\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'traffic'"
     ]
    }
   ],
   "source": [
    "\n",
    "#### 自相关和部分自相关图\n",
    "# 自相关图(ACF图)显示时间序列与其自身滞后的相关性. 每条垂直线(在自相关图上)表示系列与滞后0之间的滞后之间的相关性.\n",
    "\n",
    "import numpy as np \n",
    "import matplotlib as mpl \n",
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns \n",
    "import matplotlib\n",
    "from pywaffle import Waffle\n",
    "import pandas as pd  \n",
    "\n",
    "from statsmodels.graphics.tsaplots import plot_acf,plot_pacf\n",
    "\n",
    "# Import Data\n",
    "df = pd.read_csv(r\"F:/PythonProject/AI/Python/DataAnalysis/datasets/AirPassengers.csv\")\n",
    "\n",
    "# Draw Plot\n",
    "fig,(ax1,ax2) = plt.subplots(1,2,figsize=(16,6),dpi = 80)\n",
    "plot_acf(df.traffic.tolist(),ax=ax1,lags = 50)\n",
    "plot_pacf(df.traffic.tolist(),ax=ax2,lags=20)\n",
    "\n",
    "# Decorate\n",
    "# lighten the borders\n",
    "ax1.spines[\"top\"].set_alpha(.3); ax2.spines[\"top\"].set_alpha(.3)\n",
    "ax1.spines[\"bottom\"].set_alpha(.3); ax2.spines[\"bottom\"].set_alpha(.3)\n",
    "ax1.spines[\"right\"].set_alpha(.3); ax2.spines[\"right\"].set_alpha(.3)\n",
    "ax1.spines[\"left\"].set_alpha(.3); ax2.spines[\"left\"].set_alpha(.3)\n",
    "\n",
    "# font size of tick labels\n",
    "ax1.tick_params(axis='both',labelsize=12)\n",
    "ax2.tick_params(axis='both',labelsize=12)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x560 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "#### 交叉相关图(Cross Correlation plot)\n",
    "# 交叉相关图显示了两个时间序列相互之间的滞后.\n",
    "import statsmodels.tsa.stattools as stattools\n",
    "\n",
    "# Import Data\n",
    "df = pd.read_csv(r\"F:/PythonProject/AI/Python/DataAnalysis/datasets/mortality.csv\")\n",
    "\n",
    "x = df['mdeaths']\n",
    "y = df['fdeaths']\n",
    "\n",
    "# Compute Cross Correlations\n",
    "ccs = stattools.ccf(x,y)[:100]\n",
    "nlags = len(ccs)\n",
    "\n",
    "# Compute the Significance level\n",
    "conf_level = 2 / np.sqrt(nlags)\n",
    "\n",
    "# Draw Plot\n",
    "plt.figure(figsize=(12,7),dpi=80)\n",
    "\n",
    "plt.hlines(0,xmin=0,xmax=100,color='gray')\n",
    "plt.hlines(conf_level,xmin=0,xmax=100,color='gray')\n",
    "plt.hlines(-conf_level,xmin=0,xmax=100,color='gray')\n",
    "\n",
    "plt.bar(x=np.arange(len(ccs)),height=ccs,width=.3)\n",
    "\n",
    "# Decoration\n",
    "plt.title('$Cross\\; Correlation\\; Plot:\\; mdeaths\\; vs\\; fdeaths$',fontsize=22)\n",
    "plt.xlim(0,len(ccs))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'traffic'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32mf:\\pythonproject\\env\\scriptenv\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   2656\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2657\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2658\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'traffic'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-5-6daf282fe903>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     12\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     13\u001b[0m \u001b[1;31m# Decompose\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 14\u001b[1;33m \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mseasonal_decompose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'traffic'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'multiplicative'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     15\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     16\u001b[0m \u001b[1;31m# Plot\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mf:\\pythonproject\\env\\scriptenv\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   2925\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2926\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2927\u001b[1;33m             \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2928\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2929\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mf:\\pythonproject\\env\\scriptenv\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   2657\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2658\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2659\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2660\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2661\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msize\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'traffic'"
     ]
    }
   ],
   "source": [
    "\n",
    "#### 时间序列分解图\n",
    "# 时间序列分解图显示时间序列分解为趋势,季节和残差分量.\n",
    "from statsmodels.tsa.seasonal import seasonal_decompose\n",
    "from dateutil.parser import parse\n",
    "\n",
    "# Import Data\n",
    "df = pd.read_csv(r\"F:/PythonProject/AI/Python/DataAnalysis/datasets/AirPassengers.csv\")\n",
    "\n",
    "dates = pd.DatetimeIndex([parse(d).strftime('%Y-%m-01') for d in df['date']])\n",
    "df.set_index(dates,inplace=True)\n",
    "\n",
    "# Decompose\n",
    "result = seasonal_decompose(df['traffic'],model='multiplicative')\n",
    "\n",
    "# Plot\n",
    "plt.rcParams.update({'figure.figsize': (10,10)})\n",
    "result.plot().suptitle('Time Series Decomposition of Air Passengers')\n",
    "plt.show()"
   ]
  }
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